{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/relu/papers/52","list_of":"/method/relu","method":"ReLU","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":52,"pages_in_order":104,"rows_per_page":100,"rows":[5101,5200],"of":10350,"counts":{"archive_papers_tagged":10350,"with_a_code_link":4256,"where_syntology_ran_a_sample":1079,"not_listed_spam_title":0,"listed":10350,"listed_where_code_ran":1079,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":909,"every_run_a_failure_of_syntologys_instrument":170,"listed_with_a_run_with_no_instrument_failure":909,"listed_every_run_a_failure_of_syntologys_instrument":170,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/relu","prev":"/method/relu/papers/51","next":"/method/relu/papers/53","papers":[{"paper":null,"slug":"automatic-ship-classification-utilizing-bag","title":"Automatic Ship Classification Utilizing Bag of Deep Features","date":"2021-02-23","arxiv_id":"2102.11520","n_code_links":0,"syntology":null},{"paper":"/paper/histo-fetch-on-the-fly-processing-of","slug":"histo-fetch-on-the-fly-processing-of","title":"Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training","date":"2021-02-23","arxiv_id":"2102.11433","n_code_links":1,"syntology":null},{"paper":null,"slug":"non-singular-adversarial-robustness-of-neural","title":"Non-Singular Adversarial Robustness of Neural Networks","date":"2021-02-23","arxiv_id":"2102.11935","n_code_links":0,"syntology":null},{"paper":"/paper/sise-pc-semi-supervised-image-subsampling-for","slug":"sise-pc-semi-supervised-image-subsampling-for","title":"SISE-PC: Semi-supervised Image Subsampling for Explainable Pathology","date":"2021-02-23","arxiv_id":"2102.11560","n_code_links":1,"syntology":null},{"paper":null,"slug":"wavelet-transform-analytics-for-rf-based-uav","title":"Wavelet Transform Analytics for RF-Based UAV Detection and Identification System Using Machine Learning","date":"2021-02-23","arxiv_id":"2102.11894","n_code_links":0,"syntology":null},{"paper":"/paper/cstr-a-classification-perspective-on-scene","slug":"cstr-a-classification-perspective-on-scene","title":"Revisiting Classification Perspective on Scene Text Recognition","date":"2021-02-22","arxiv_id":"2102.10884","n_code_links":1,"syntology":null},{"paper":null,"slug":"escaping-from-zero-gradient-revisiting-action","title":"Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization","date":"2021-02-22","arxiv_id":"2102.11055","n_code_links":0,"syntology":null},{"paper":"/paper/lightweight-combinational-machine-learning","slug":"lightweight-combinational-machine-learning","title":"Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs","date":"2021-02-22","arxiv_id":"2102.11385","n_code_links":1,"syntology":null},{"paper":"/paper/lvcnet-efficient-condition-dependent-modeling","slug":"lvcnet-efficient-condition-dependent-modeling","title":"LVCNet: Efficient Condition-Dependent Modeling Network for Waveform Generation","date":"2021-02-22","arxiv_id":"2102.10815","n_code_links":5,"syntology":null},{"paper":"/paper/accelerated-sim-to-real-deep-reinforcement","slug":"accelerated-sim-to-real-deep-reinforcement","title":"Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player","date":"2021-02-21","arxiv_id":"2102.10711","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-relu-networks-preserve-expected-length","title":"Deep ReLU Networks Preserve Expected Length","date":"2021-02-21","arxiv_id":"2102.10492","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-custom-modality-specific-u-net","title":"Improved Semantic Segmentation of Tuberculosis-consistent findings in Chest X-rays Using Augmented Training of Modality-specific U-Net Models with Weak Localizations","date":"2021-02-21","arxiv_id":"2102.10607","n_code_links":0,"syntology":null},{"paper":null,"slug":"emds-5-environmental-microorganism-image","title":"EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks","date":"2021-02-20","arxiv_id":"2102.10370","n_code_links":0,"syntology":null},{"paper":"/paper/squeeze-and-excitation-normalization-for","slug":"squeeze-and-excitation-normalization-for","title":"Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images","date":"2021-02-20","arxiv_id":"2102.10446","n_code_links":1,"syntology":null},{"paper":"/paper/ssfg-stochastically-scaling-features-and","slug":"ssfg-stochastically-scaling-features-and","title":"SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks","date":"2021-02-20","arxiv_id":"2102.10338","n_code_links":1,"syntology":null},{"paper":null,"slug":"lottery-ticket-implies-accuracy-degradation","title":"Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?","date":"2021-02-19","arxiv_id":"2102.11068","n_code_links":0,"syntology":null},{"paper":"/paper/training-cascaded-networks-for-speeded","slug":"training-cascaded-networks-for-speeded","title":"Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss","date":"2021-02-19","arxiv_id":"2102.09808","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-mathematical-principle-of-deep-learning","title":"A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space","date":"2021-02-18","arxiv_id":"2102.09235","n_code_links":0,"syntology":null},{"paper":null,"slug":"benefits-of-linear-conditioning-for","title":"Benefits of Linear Conditioning with Metadata for Image Segmentation","date":"2021-02-18","arxiv_id":"2102.09582","n_code_links":0,"syntology":null},{"paper":"/paper/densely-nested-top-down-flows-for-salient","slug":"densely-nested-top-down-flows-for-salient","title":"Densely Nested Top-Down Flows for Salient Object Detection","date":"2021-02-18","arxiv_id":"2102.09133","n_code_links":1,"syntology":null},{"paper":"/paper/going-full-tilt-boogie-on-document","slug":"going-full-tilt-boogie-on-document","title":"Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer","date":"2021-02-18","arxiv_id":"2102.09550","n_code_links":1,"syntology":null},{"paper":"/paper/image-compositing-for-segmentation-of","slug":"image-compositing-for-segmentation-of","title":"Image Compositing for Segmentation of Surgical Tools without Manual Annotations","date":"2021-02-18","arxiv_id":"2102.09528","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"minimizing-false-negative-rate-in-melanoma","title":"Minimizing false negative rate in melanoma detection and providing insight into the causes of classification","date":"2021-02-18","arxiv_id":"2102.09199","n_code_links":0,"syntology":null},{"paper":"/paper/recurrent-rational-networks","slug":"recurrent-rational-networks","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","date":"2021-02-18","arxiv_id":"2102.09407","n_code_links":4,"syntology":{"ran":6,"of":6,"n_ran_checked":0,"n_instrument":6,"unverified":0,"pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ml-research/rational_activations","ml-research/rational_rl","ml-research/rational_sl","k4ntz/activation-functions"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-dataset-and-benchmark-for-malaria-life","title":"A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images","date":"2021-02-17","arxiv_id":"2102.08708","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensemble-transfer-learning-of-elastography","title":"Ensemble Transfer Learning of Elastography and B-mode Breast Ultrasound Images","date":"2021-02-17","arxiv_id":"2102.08567","n_code_links":0,"syntology":null},{"paper":"/paper/lambdanetworks-modeling-long-range-1","slug":"lambdanetworks-modeling-long-range-1","title":"LambdaNetworks: Modeling Long-Range Interactions Without Attention","date":"2021-02-17","arxiv_id":"2102.08602","n_code_links":7,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"rethinking-co-design-of-neural-architectures","title":"Rethinking Co-design of Neural Architectures and Hardware Accelerators","date":"2021-02-17","arxiv_id":"2102.08619","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multiscale-graph-convolutional-network-for","title":"A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images","date":"2021-02-16","arxiv_id":"2102.08041","n_code_links":0,"syntology":null},{"paper":"/paper/an-automl-based-approach-to-multimodal-image","slug":"an-automl-based-approach-to-multimodal-image","title":"An AutoML-based Approach to Multimodal Image Sentiment Analysis","date":"2021-02-16","arxiv_id":"2102.08092","n_code_links":0,"syntology":null},{"paper":null,"slug":"axial-residual-networks-for-cyclegan-based","title":"Axial Residual Networks for CycleGAN-based Voice Conversion","date":"2021-02-16","arxiv_id":"2102.08075","n_code_links":0,"syntology":null},{"paper":null,"slug":"complex-momentum-for-learning-in-games","title":"Complex Momentum for Optimization in Games","date":"2021-02-16","arxiv_id":"2102.08431","n_code_links":0,"syntology":null},{"paper":"/paper/improving-deep-learning-based-semi-supervised","slug":"improving-deep-learning-based-semi-supervised","title":"Comparison of semi-supervised deep learning algorithms for audio classification","date":"2021-02-16","arxiv_id":"2102.08183","n_code_links":1,"syntology":null},{"paper":null,"slug":"resnet-lddmm-advancing-the-lddmm-framework","title":"ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual Networks","date":"2021-02-16","arxiv_id":"2102.07951","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-larger-networks-for-deep","title":"Training Larger Networks for Deep Reinforcement Learning","date":"2021-02-16","arxiv_id":"2102.07920","n_code_links":0,"syntology":null},{"paper":null,"slug":"colored-kimia-path24-dataset-configurations","title":"Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings","date":"2021-02-15","arxiv_id":"2102.07611","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-severity-classification-of","title":"Detection and severity classification of COVID-19 in CT images using deep learning","date":"2021-02-15","arxiv_id":"2102.07726","n_code_links":0,"syntology":null},{"paper":"/paper/momentum-residual-neural-networks","slug":"momentum-residual-neural-networks","title":"Momentum Residual Neural Networks","date":"2021-02-15","arxiv_id":"2102.07870","n_code_links":1,"syntology":null},{"paper":null,"slug":"naturalizing-neuromorphic-vision-event","title":"Naturalizing Neuromorphic Vision Event Streams Using GANs","date":"2021-02-14","arxiv_id":"2102.07243","n_code_links":0,"syntology":null},{"paper":null,"slug":"rf-pix2pix-unsupervised-wi-fi-to-video","title":"RF PIX2PIX Unsupervised Wi-Fi to Video Translation","date":"2021-02-14","arxiv_id":"2102.09345","n_code_links":0,"syntology":null},{"paper":"/paper/fast-accurate-barcode-detection-in-ultra-high","slug":"fast-accurate-barcode-detection-in-ultra-high","title":"Fast, Accurate Barcode Detection in Ultra High-Resolution Images","date":"2021-02-13","arxiv_id":"2102.06868","n_code_links":0,"syntology":null},{"paper":"/paper/how-framelets-enhance-graph-neural-networks","slug":"how-framelets-enhance-graph-neural-networks","title":"How Framelets Enhance Graph Neural Networks","date":"2021-02-13","arxiv_id":"2102.06986","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":0,"n_instrument":4,"unverified":2,"pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","official":{"repos":["YuGuangWang/UFG"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"on-the-convergence-of-group-sparse","title":"On the convergence of group-sparse autoencoders","date":"2021-02-13","arxiv_id":"2102.07003","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-into-the-codec-noise-robust-speech","title":"Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders","date":"2021-02-12","arxiv_id":"2102.06610","n_code_links":0,"syntology":null},{"paper":null,"slug":"vara-tts-non-autoregressive-text-to-speech","title":"VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention","date":"2021-02-12","arxiv_id":"2102.06431","n_code_links":0,"syntology":null},{"paper":null,"slug":"aboships-an-inshore-and-offshore-maritime","title":"ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations","date":"2021-02-11","arxiv_id":"2102.05869","n_code_links":0,"syntology":null},{"paper":"/paper/robust-policy-gradient-against-strong-data","slug":"robust-policy-gradient-against-strong-data","title":"Robust Policy Gradient against Strong Data Corruption","date":"2021-02-11","arxiv_id":"2102.05800","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-deepsentinel-an-extensible-corpus-of","title":"Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a general-purpose sensor-fusion semantic embedding model","date":"2021-02-11","arxiv_id":"2102.06260","n_code_links":0,"syntology":null},{"paper":null,"slug":"adafuse-adaptive-temporal-fusion-network-for-1","title":"AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition","date":"2021-02-10","arxiv_id":"2102.05775","n_code_links":0,"syntology":null},{"paper":null,"slug":"agnostic-proper-learning-of-halfspaces-under","title":"Agnostic Proper Learning of Halfspaces under Gaussian Marginals","date":"2021-02-10","arxiv_id":"2102.05629","n_code_links":0,"syntology":null},{"paper":null,"slug":"application-of-yolo-on-mask-detection-task","title":"Application of Yolo on Mask Detection Task","date":"2021-02-10","arxiv_id":"2102.05402","n_code_links":0,"syntology":null},{"paper":"/paper/brecq-pushing-the-limit-of-post-training-1","slug":"brecq-pushing-the-limit-of-post-training-1","title":"BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction","date":"2021-02-10","arxiv_id":"2102.05426","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":2,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yhhhli/BRECQ"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/d2a-u-net-automatic-segmentation-of-covid-19","slug":"d2a-u-net-automatic-segmentation-of-covid-19","title":"D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism","date":"2021-02-10","arxiv_id":"2102.05210","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-with-symmetric","title":"Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users","date":"2021-02-10","arxiv_id":"2103.13298","n_code_links":0,"syntology":null},{"paper":"/paper/improving-model-based-reinforcement-learning","slug":"improving-model-based-reinforcement-learning","title":"Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision","date":"2021-02-10","arxiv_id":"2102.05599","n_code_links":2,"syntology":null},{"paper":"/paper/pruning-of-convolutional-neural-networks","slug":"pruning-of-convolutional-neural-networks","title":"Pruning of Convolutional Neural Networks Using Ising Energy Model","date":"2021-02-10","arxiv_id":"2102.05437","n_code_links":1,"syntology":null},{"paper":"/paper/regional-attention-with-architecture-rebuilt","slug":"regional-attention-with-architecture-rebuilt","title":"Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition","date":"2021-02-10","arxiv_id":"2102.05348","n_code_links":1,"syntology":null},{"paper":null,"slug":"searching-for-fast-model-families-on","title":"Searching for Fast Model Families on Datacenter Accelerators","date":"2021-02-10","arxiv_id":"2102.05610","n_code_links":0,"syntology":null},{"paper":null,"slug":"distribution-adaptive-int8-quantization-for","title":"Distribution Adaptive INT8 Quantization for Training CNNs","date":"2021-02-09","arxiv_id":"2102.04782","n_code_links":0,"syntology":null},{"paper":"/paper/mali-a-memory-efficient-and-reverse-accurate-1","slug":"mali-a-memory-efficient-and-reverse-accurate-1","title":"MALI: A memory efficient and reverse accurate integrator for Neural ODEs","date":"2021-02-09","arxiv_id":"2102.04668","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["juntang-zhuang/TorchDiffEqPack"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"train-a-one-million-way-instance-classifier","title":"Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning","date":"2021-02-09","arxiv_id":"2102.04848","n_code_links":0,"syntology":null},{"paper":null,"slug":"when-does-gradient-descent-with-logistic-loss-1","title":"When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?","date":"2021-02-09","arxiv_id":"2102.04998","n_code_links":0,"syntology":null},{"paper":"/paper/a-mixed-focal-loss-function-for-handling","slug":"a-mixed-focal-loss-function-for-handling","title":"Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation","date":"2021-02-08","arxiv_id":"2102.04525","n_code_links":5,"syntology":null},{"paper":null,"slug":"aps-a-large-scale-multi-modal-indoor-camera","title":"APS: A Large-Scale Multi-Modal Indoor Camera Positioning System","date":"2021-02-08","arxiv_id":"2102.04139","n_code_links":0,"syntology":null},{"paper":"/paper/partition-based-formulations-for-mixed","slug":"partition-based-formulations-for-mixed","title":"Partition-based formulations for mixed-integer optimization of trained ReLU neural networks","date":"2021-02-08","arxiv_id":"2102.04373","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","official":{"repos":["cog-imperial/partitionedformulations_nn"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":"/paper/single-image-super-resolution-using-residual","slug":"single-image-super-resolution-using-residual","title":"Single Image Super-Resolution using Residual Channel Attention Network","date":"2021-02-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/spike-based-residual-blocks","slug":"spike-based-residual-blocks","title":"Deep Residual Learning in Spiking Neural Networks","date":"2021-02-08","arxiv_id":"2102.04159","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["fangwei123456/Spike-Element-Wise-ResNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["community"]}}},{"paper":"/paper/transunet-transformers-make-strong-encoders","slug":"transunet-transformers-make-strong-encoders","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","date":"2021-02-08","arxiv_id":"2102.04306","n_code_links":22,"syntology":{"ran":6,"of":7,"n_ran_checked":2,"n_instrument":4,"unverified":1,"pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Beckschen/TransUNet"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/self-supervised-driven-consistency-training","slug":"self-supervised-driven-consistency-training","title":"Self-supervised driven consistency training for annotation efficient histopathology image analysis","date":"2021-02-07","arxiv_id":"2102.03897","n_code_links":2,"syntology":null},{"paper":"/paper/explainable-reinforcement-learning-for","slug":"explainable-reinforcement-learning-for","title":"Explainable Reinforcement Learning for Longitudinal Control","date":"2021-02-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"convolutional-neural-network-interpretability","title":"Convolutional Neural Network Interpretability with General Pattern Theory","date":"2021-02-05","arxiv_id":"2102.04247","n_code_links":0,"syntology":null},{"paper":"/paper/gnn-rl-compression-topology-aware-network","slug":"gnn-rl-compression-topology-aware-network","title":"Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning","date":"2021-02-05","arxiv_id":"2102.03214","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yusx-swapp/gnn-rl-model-compression"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/on-the-connection-of-benford-s-law-and-neural","slug":"on-the-connection-of-benford-s-law-and-neural","title":"Rethinking Neural Networks With Benford's Law","date":"2021-02-05","arxiv_id":"2102.03313","n_code_links":1,"syntology":null},{"paper":"/paper/real-time-denoising-and-dereverberation-with","slug":"real-time-denoising-and-dereverberation-with","title":"Real-time Denoising and Dereverberation with Tiny Recurrent U-Net","date":"2021-02-05","arxiv_id":"2102.03207","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-survey-of-motion-planning-algorithms-for","title":"A review of motion planning algorithms for intelligent robotics","date":"2021-02-04","arxiv_id":"2102.02376","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-semiparametric-language-models","title":"Adaptive Semiparametric Language Models","date":"2021-02-04","arxiv_id":"2102.02557","n_code_links":0,"syntology":null},{"paper":"/paper/fine-tuning-deep-learning-model-parameters","slug":"fine-tuning-deep-learning-model-parameters","title":"Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge","date":"2021-02-04","arxiv_id":"2102.02711","n_code_links":1,"syntology":null},{"paper":"/paper/ml-doctor-holistic-risk-assessment-of","slug":"ml-doctor-holistic-risk-assessment-of","title":"ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models","date":"2021-02-04","arxiv_id":"2102.02551","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["liuyugeng/ml-doctor"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"deep-cnns-for-large-scale-species","title":"Deep CNNs for large scale species classification","date":"2021-02-03","arxiv_id":"2102.01863","n_code_links":0,"syntology":null},{"paper":"/paper/monaural-speech-enhancement-with-complex","slug":"monaural-speech-enhancement-with-complex","title":"Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses","date":"2021-02-03","arxiv_id":"2102.01993","n_code_links":2,"syntology":null},{"paper":null,"slug":"on-the-approximation-power-of-two-layer","title":"On the Approximation Power of Two-Layer Networks of Random ReLUs","date":"2021-02-03","arxiv_id":"2102.02336","n_code_links":0,"syntology":null},{"paper":"/paper/pitfalls-of-static-language-modelling","slug":"pitfalls-of-static-language-modelling","title":"Mind the Gap: Assessing Temporal Generalization in Neural Language Models","date":"2021-02-03","arxiv_id":"2102.01951","n_code_links":1,"syntology":null},{"paper":"/paper/anomalous-event-recognition-in-videos-based","slug":"anomalous-event-recognition-in-videos-based","title":"Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures","date":"2021-02-02","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"face-recognition-using-sf-3-cnn-with-higher","title":"Face Recognition Using $Sf_{3}CNN$ With Higher Feature Discrimination","date":"2021-02-02","arxiv_id":"2102.01404","n_code_links":0,"syntology":null},{"paper":"/paper/generating-images-from-caption-and-vice-versa","slug":"generating-images-from-caption-and-vice-versa","title":"Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search","date":"2021-02-02","arxiv_id":"2102.01645","n_code_links":3,"syntology":null},{"paper":"/paper/psla-improving-audio-event-classification","slug":"psla-improving-audio-event-classification","title":"PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation","date":"2021-02-02","arxiv_id":"2102.01243","n_code_links":1,"syntology":null},{"paper":"/paper/an-end-to-end-trainable-iterative-network","slug":"an-end-to-end-trainable-iterative-network","title":"An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction","date":"2021-02-01","arxiv_id":"2102.00783","n_code_links":1,"syntology":null},{"paper":"/paper/convnets-for-counting-object-detection-of","slug":"convnets-for-counting-object-detection-of","title":"ConvNets for Counting: Object Detection of Transient Phenomena in Steelpan Drums","date":"2021-02-01","arxiv_id":"2102.00632","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-high-resolution-network-for-low-dose-x","title":"Deep High-Resolution Network for Low Dose X-ray CT Denoising","date":"2021-02-01","arxiv_id":"2102.00599","n_code_links":0,"syntology":null},{"paper":"/paper/densely-connected-recurrent-residual-dense","slug":"densely-connected-recurrent-residual-dense","title":"Densely Connected Recurrent Residual (Dense R2UNet) Convolutional Neural Network for Segmentation of Lung CT Images","date":"2021-02-01","arxiv_id":"2102.00663","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-network-architectures-to-classify","title":"Neural Network architectures to classify emotions in Indian Classical Music","date":"2021-02-01","arxiv_id":"2102.00616","n_code_links":0,"syntology":null},{"paper":"/paper/rectinet-v2-a-stacked-network-architecture","slug":"rectinet-v2-a-stacked-network-architecture","title":"RectiNet-v2: A stacked network architecture for document image dewarping","date":"2021-02-01","arxiv_id":"2102.01120","n_code_links":0,"syntology":null},{"paper":"/paper/spatio-temporal-weather-forecasting-and","slug":"spatio-temporal-weather-forecasting-and","title":"Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms","date":"2021-02-01","arxiv_id":"2102.00696","n_code_links":2,"syntology":null},{"paper":null,"slug":"toon2real-translating-cartoon-images-to","title":"toon2real: Translating Cartoon Images to Realistic Images","date":"2021-02-01","arxiv_id":"2102.01143","n_code_links":0,"syntology":null},{"paper":"/paper/underwater-image-enhancement-via-learning","slug":"underwater-image-enhancement-via-learning","title":"Underwater Image Enhancement via Learning Water Type Desensitized Representations","date":"2021-02-01","arxiv_id":"2102.00676","n_code_links":1,"syntology":null},{"paper":"/paper/classification-of-fracture-and-normal","slug":"classification-of-fracture-and-normal","title":"Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models","date":"2021-01-31","arxiv_id":"2102.00515","n_code_links":0,"syntology":null},{"paper":"/paper/computational-performance-predictions-for","slug":"computational-performance-predictions-for","title":"A Runtime-Based Computational Performance Predictor for Deep Neural Network Training","date":"2021-01-31","arxiv_id":"2102.00527","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-networks-with-complex-valued-weights","title":"Neural Networks with Complex-Valued Weights Have No Spurious Local Minima","date":"2021-01-31","arxiv_id":"2103.07287","n_code_links":0,"syntology":null},{"paper":"/paper/re-reproducibility-report-of-interpretable","slug":"re-reproducibility-report-of-interpretable","title":"[Re] Reproducibility report of \"Interpretable Complex-Valued Neural Networks for Privacy Protection\"","date":"2021-01-31","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-model-compression-based-on-the-training","title":"Deep Model Compression based on the Training History","date":"2021-01-30","arxiv_id":"2102.00160","n_code_links":0,"syntology":null}],"record_sha256":"43f9343ce750cd0857cb1c30b389a38209d550368f8c780177aeafa2808890ec","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}