{"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/sigmoid-activation/papers/15","list_of":"/method/sigmoid-activation","method":"Sigmoid Activation","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":15,"pages_in_order":72,"rows_per_page":100,"rows":[1401,1500],"of":7112,"counts":{"archive_papers_tagged":7112,"with_a_code_link":2470,"where_syntology_ran_a_sample":461,"not_listed_spam_title":0,"listed":7112,"listed_where_code_ran":461,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":386,"every_run_a_failure_of_syntologys_instrument":75,"listed_with_a_run_with_no_instrument_failure":386,"listed_every_run_a_failure_of_syntologys_instrument":75,"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/sigmoid-activation","prev":"/method/sigmoid-activation/papers/14","next":"/method/sigmoid-activation/papers/16","papers":[{"paper":null,"slug":"adversarial-conversational-shaping-for","title":"Adversarial Conversational Shaping for Intelligent Agents","date":"2023-07-20","arxiv_id":"2307.11785","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-adaptive-dual-level-reinforcement-learning","title":"An Adaptive Dual-level Reinforcement Learning Approach for Optimal Trade Execution","date":"2023-07-20","arxiv_id":"2307.10649","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-between-transformers-and","title":"Comparison between transformers and convolutional models for fine-grained classification of insects","date":"2023-07-20","arxiv_id":"2307.11112","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-arbitrary-mobile-sensor","title":"Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction","date":"2023-07-20","arxiv_id":"2307.11793","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-fast-and-map-free-model-for-trajectory","title":"A Fast and Map-Free Model for Trajectory Prediction in Traffics","date":"2023-07-19","arxiv_id":"2307.09831","n_code_links":0,"syntology":null},{"paper":"/paper/a-matrix-ensemble-kalman-filter-based-multi","slug":"a-matrix-ensemble-kalman-filter-based-multi","title":"A Matrix Ensemble Kalman Filter-based Multi-arm Neural Network to Adequately Approximate Deep Neural Networks","date":"2023-07-19","arxiv_id":"2307.10436","n_code_links":1,"syntology":null},{"paper":null,"slug":"perturbing-a-neural-network-to-infer","title":"Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data","date":"2023-07-19","arxiv_id":"2307.09770","n_code_links":0,"syntology":null},{"paper":"/paper/tunes-a-temporal-u-net-with-self-attention","slug":"tunes-a-temporal-u-net-with-self-attention","title":"TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition","date":"2023-07-19","arxiv_id":"2307.09997","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparative-analysis-of-sr-gan-models","title":"A comparative analysis of SRGAN models","date":"2023-07-18","arxiv_id":"2307.09456","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-machine-learning-for-extraction-of","slug":"multimodal-machine-learning-for-extraction-of","title":"Modular Multimodal Machine Learning for Extraction of Theorems and Proofs in Long Scientific Documents (Extended Version)","date":"2023-07-18","arxiv_id":"2307.09047","n_code_links":2,"syntology":null},{"paper":"/paper/repvit-revisiting-mobile-cnn-from-vit","slug":"repvit-revisiting-mobile-cnn-from-vit","title":"RepViT: Revisiting Mobile CNN From ViT Perspective","date":"2023-07-18","arxiv_id":"2307.09283","n_code_links":8,"syntology":{"ran":6,"of":12,"n_ran_checked":4,"n_instrument":2,"unverified":6,"pointer_only":5,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":"/paper/efficient-prediction-of-peptide-self-assembly","slug":"efficient-prediction-of-peptide-self-assembly","title":"Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding","date":"2023-07-17","arxiv_id":"2307.09169","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-selective-attention-lstm-for-well","title":"Efficient selective attention LSTM for well log curve synthesis","date":"2023-07-17","arxiv_id":"2307.10253","n_code_links":0,"syntology":null},{"paper":null,"slug":"operator-guidance-informed-by-ai-augmented","title":"Operator Guidance Informed by AI-Augmented Simulations","date":"2023-07-17","arxiv_id":"2307.08810","n_code_links":0,"syntology":null},{"paper":null,"slug":"pat-parallel-attention-transformer-for-visual","title":"PAT: Parallel Attention Transformer for Visual Question Answering in Vietnamese","date":"2023-07-17","arxiv_id":"2307.08247","n_code_links":0,"syntology":null},{"paper":"/paper/generative-meta-learning-robust-quality","slug":"generative-meta-learning-robust-quality","title":"Generative Meta-Learning Robust Quality-Diversity Portfolio","date":"2023-07-15","arxiv_id":"2307.07811","n_code_links":2,"syntology":null},{"paper":null,"slug":"political-sentiment-analysis-of-persian","title":"Political Sentiment Analysis of Persian Tweets Using CNN-LSTM Model","date":"2023-07-15","arxiv_id":"2307.07740","n_code_links":0,"syntology":null},{"paper":null,"slug":"combining-multitemporal-optical-and-sar-data","title":"Combining multitemporal optical and SAR data for LAI imputation with BiLSTM network","date":"2023-07-14","arxiv_id":"2307.07434","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-for-option-pricing-an","title":"Machine learning for option pricing: an empirical investigation of network architectures","date":"2023-07-14","arxiv_id":"2307.07657","n_code_links":0,"syntology":null},{"paper":null,"slug":"reconstruction-of-3-axis-seismocardiogram","title":"Reconstruction of 3-Axis Seismocardiogram from Right-to-left and Head-to-foot Components Using A Long Short-Term Memory Network","date":"2023-07-14","arxiv_id":"2307.07566","n_code_links":0,"syntology":null},{"paper":null,"slug":"critical-comparisons-on-deep-learning","title":"Critical comparisons on deep learning approaches for foreign exchange rate prediction","date":"2023-07-13","arxiv_id":"2307.06600","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-the-presence-of-covid-19","title":"Detecting the Presence of COVID-19 Vaccination Hesitancy from South African Twitter Data Using Machine Learning","date":"2023-07-12","arxiv_id":"2307.15072","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-generation-of-semantic-parts-for","slug":"automatic-generation-of-semantic-parts-for","title":"Automatic Generation of Semantic Parts for Face Image Synthesis","date":"2023-07-11","arxiv_id":"2307.05317","n_code_links":1,"syntology":null},{"paper":null,"slug":"hybrid-hidden-markov-lstm-for-short-term","title":"Hybrid hidden Markov LSTM for short-term traffic flow prediction","date":"2023-07-11","arxiv_id":"2307.04954","n_code_links":0,"syntology":null},{"paper":null,"slug":"line-art-colorization-of-fakemon-using","title":"Line Art Colorization of Fakemon using Generative Adversarial Neural Networks","date":"2023-07-11","arxiv_id":"2307.05760","n_code_links":0,"syntology":null},{"paper":null,"slug":"point-to-the-hidden-exposing-speech-audio","title":"Point to the Hidden: Exposing Speech Audio Splicing via Signal Pointer Nets","date":"2023-07-11","arxiv_id":"2307.05641","n_code_links":0,"syntology":null},{"paper":null,"slug":"sephrnet-generating-high-resolution-crop-maps","title":"SepHRNet: Generating High-Resolution Crop Maps from Remote Sensing imagery using HRNet with Separable Convolution","date":"2023-07-11","arxiv_id":"2307.05700","n_code_links":0,"syntology":null},{"paper":"/paper/writer-adaptation-for-offline-text","slug":"writer-adaptation-for-offline-text","title":"Writer adaptation for offline text recognition: An exploration of neural network-based methods","date":"2023-07-11","arxiv_id":"2307.15071","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-self-attention-causal-lstm-model-for","title":"A Self-Attention Causal LSTM Model for Precipitation Nowcasting","date":"2023-07-10","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"emotion-analysis-on-eeg-signal-using-machine","title":"Emotion Analysis on EEG Signal Using Machine Learning and Neural Network","date":"2023-07-09","arxiv_id":"2307.05375","n_code_links":0,"syntology":null},{"paper":null,"slug":"marine-debris-detection-in-satellite","title":"Marine Debris Detection in Satellite Surveillance using Attention Mechanisms","date":"2023-07-09","arxiv_id":"2307.04128","n_code_links":0,"syntology":null},{"paper":"/paper/edge-aware-mirror-network-for-camouflaged","slug":"edge-aware-mirror-network-for-camouflaged","title":"Edge-Aware Mirror Network for Camouflaged Object Detection","date":"2023-07-08","arxiv_id":"2307.03932","n_code_links":1,"syntology":null},{"paper":null,"slug":"intformer-a-time-embedded-attention-based","title":"inTformer: A Time-Embedded Attention-Based Transformer for Crash Likelihood Prediction at Intersections Using Connected Vehicle Data","date":"2023-07-07","arxiv_id":"2307.03854","n_code_links":0,"syntology":null},{"paper":null,"slug":"art-authentication-with-vision-transformers","title":"Art Authentication with Vision Transformers","date":"2023-07-06","arxiv_id":"2307.03039","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-raw-waveforms-with-deep-learning","title":"Evaluating raw waveforms with deep learning frameworks for speech emotion recognition","date":"2023-07-06","arxiv_id":"2307.02820","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-graph-for-nlg-in-the-context-of","title":"Knowledge Graph for NLG in the context of conversational agents","date":"2023-07-04","arxiv_id":"2307.01548","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-branch-in-combinatorial","title":"Learning to Branch in Combinatorial Optimization with Graph Pointer Networks","date":"2023-07-04","arxiv_id":"2307.01434","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-channel-feature-extraction-for-virtual","title":"Multi-Channel Feature Extraction for Virtual Histological Staining of Photon Absorption Remote Sensing Images","date":"2023-07-04","arxiv_id":"2307.01824","n_code_links":0,"syntology":null},{"paper":"/paper/transformed-protoform-reconstruction","slug":"transformed-protoform-reconstruction","title":"Transformed Protoform Reconstruction","date":"2023-07-04","arxiv_id":"2307.01896","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-end-to-end-multi-module-audio-deepfake","title":"An End-to-End Multi-Module Audio Deepfake Generation System for ADD Challenge 2023","date":"2023-07-03","arxiv_id":"2307.00729","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-and-fully-automatic-retinal-choroid","slug":"efficient-and-fully-automatic-retinal-choroid","title":"An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography","date":"2023-07-03","arxiv_id":"2307.00904","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-tuning-pid-control-via-a-hybrid-actor","title":"Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control","date":"2023-07-03","arxiv_id":"2307.01312","n_code_links":0,"syntology":null},{"paper":null,"slug":"streamlined-lensed-quasar-identification-in","title":"Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks","date":"2023-07-03","arxiv_id":"2307.01090","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multi-task-learning-framework-for-carotid","title":"A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification","date":"2023-07-02","arxiv_id":"2307.00583","n_code_links":0,"syntology":null},{"paper":null,"slug":"decoding-taste-information-in-human-brain-a","title":"Decoding Taste Information in Human Brain: A Temporal and Spatial Reconstruction Data Augmentation Method Coupled with Taste EEG","date":"2023-07-01","arxiv_id":"2307.05365","n_code_links":0,"syntology":null},{"paper":"/paper/long-short-term-memory-with-activation-on","slug":"long-short-term-memory-with-activation-on","title":"Long short-term memory with activation on gradient","date":"2023-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/mnisq-a-large-scale-quantum-circuit-dataset","slug":"mnisq-a-large-scale-quantum-circuit-dataset","title":"MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era","date":"2023-06-29","arxiv_id":"2306.16627","n_code_links":1,"syntology":null},{"paper":"/paper/rl4co-an-extensive-reinforcement-learning-for","slug":"rl4co-an-extensive-reinforcement-learning-for","title":"RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark","date":"2023-06-29","arxiv_id":"2306.17100","n_code_links":3,"syntology":{"ran":12,"of":16,"n_ran_checked":12,"n_instrument":0,"unverified":4,"pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["ai4co/rl4co","pytorch/rl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"fast-recognition-of-birds-in-offshore-wind","title":"Fast Recognition of birds in offshore wind farms based on an improved deep learning model","date":"2023-06-28","arxiv_id":"2306.16019","n_code_links":0,"syntology":null},{"paper":"/paper/relevant-entity-selection-knowledge-graph","slug":"relevant-entity-selection-knowledge-graph","title":"Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning","date":"2023-06-28","arxiv_id":"2306.16296","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-two-stream-decision-level-fusion-of","title":"A Novel Two Stream Decision Level Fusion of Vision and Inertial Sensors Data for Automatic Multimodal Human Activity Recognition System","date":"2023-06-27","arxiv_id":"2306.15765","n_code_links":0,"syntology":null},{"paper":null,"slug":"ncis-deep-color-gradient-maps-regression-and","title":"NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images","date":"2023-06-27","arxiv_id":"2306.15784","n_code_links":0,"syntology":null},{"paper":"/paper/sar-atr-under-limited-training-data-via","slug":"sar-atr-under-limited-training-data-via","title":"SAR ATR under Limited Training Data Via MobileNetV3","date":"2023-06-27","arxiv_id":"2306.15287","n_code_links":1,"syntology":null},{"paper":null,"slug":"xai-cyclegan-a-cycle-consistent-generative","title":"xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network","date":"2023-06-27","arxiv_id":"2306.15760","n_code_links":0,"syntology":null},{"paper":"/paper/aog-lstm-an-adaptive-attention-neural-network","slug":"aog-lstm-an-adaptive-attention-neural-network","title":"AOG-LSTM: An adaptive attention neural network for visual storytelling","date":"2023-06-26","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/intercode-standardizing-and-benchmarking","slug":"intercode-standardizing-and-benchmarking","title":"InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback","date":"2023-06-26","arxiv_id":"2306.14898","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"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":{"repos":["princeton-nlp/intercode"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"comparative-study-of-predicting-stock-index","title":"Comparative Study of Predicting Stock Index Using Deep Learning Models","date":"2023-06-24","arxiv_id":"2306.13931","n_code_links":0,"syntology":null},{"paper":null,"slug":"abstractive-text-summarization-for-resumes","title":"Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM","date":"2023-06-23","arxiv_id":"2306.13315","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhanced-dengue-outbreak-prediction-in","title":"Enhanced Dengue Outbreak Prediction in Tamilnadu using Meteorological and Entomological data","date":"2023-06-23","arxiv_id":"2306.13456","n_code_links":0,"syntology":null},{"paper":"/paper/improving-panoptic-segmentation-for-nighttime","slug":"improving-panoptic-segmentation-for-nighttime","title":"Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving Scenes","date":"2023-06-23","arxiv_id":"2306.13725","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparison-of-time-based-models-for","title":"A Comparison of Time-based Models for Multimodal Emotion Recognition","date":"2023-06-22","arxiv_id":"2306.13076","n_code_links":0,"syntology":null},{"paper":"/paper/constructing-colloquial-dataset-for-persian","slug":"constructing-colloquial-dataset-for-persian","title":"Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs","date":"2023-06-22","arxiv_id":"2306.12679","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-dynamic-epidemiological-modelling-for","title":"Deep Dynamic Epidemiological Modelling for COVID-19 Forecasting in Multi-level Districts","date":"2023-06-21","arxiv_id":"2306.12457","n_code_links":0,"syntology":null},{"paper":null,"slug":"edge-devices-inference-performance-comparison","title":"Edge Devices Inference Performance Comparison","date":"2023-06-21","arxiv_id":"2306.12093","n_code_links":0,"syntology":null},{"paper":null,"slug":"lightweight-wood-panel-defect-detection","title":"Lightweight wood panel defect detection method incorporating attention mechanism and feature fusion network","date":"2023-06-21","arxiv_id":"2306.12113","n_code_links":0,"syntology":null},{"paper":null,"slug":"probing-the-limit-of-hydrologic","title":"Probing the limit of hydrologic predictability with the Transformer network","date":"2023-06-21","arxiv_id":"2306.12384","n_code_links":0,"syntology":null},{"paper":null,"slug":"sifter-a-task-specific-alignment-strategy-for","title":"SIFTER: A Task-specific Alignment Strategy for Enhancing Sentence Embeddings","date":"2023-06-21","arxiv_id":"2306.12280","n_code_links":0,"syntology":null},{"paper":"/paper/towards-accurate-translation-via-semantically","slug":"towards-accurate-translation-via-semantically","title":"Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints","date":"2023-06-21","arxiv_id":"2306.12089","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-deep-learning-model-for-heterogeneous","title":"A Deep Learning Model for Heterogeneous Dataset Analysis -- Application to Winter Wheat Crop Yield Prediction","date":"2023-06-20","arxiv_id":"2306.11942","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatio-temporal-deepkriging-for-interpolation","title":"Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting","date":"2023-06-20","arxiv_id":"2306.11472","n_code_links":0,"syntology":null},{"paper":"/paper/renderers-are-good-zero-shot-representation","slug":"renderers-are-good-zero-shot-representation","title":"Renderers are Good Zero-Shot Representation Learners: Exploring Diffusion Latents for Metric Learning","date":"2023-06-19","arxiv_id":"2306.10721","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-open-domain-keyphrase-generation","title":"Unsupervised Open-domain Keyphrase Generation","date":"2023-06-19","arxiv_id":"2306.10755","n_code_links":0,"syntology":null},{"paper":null,"slug":"federated-learning-based-distributed","title":"Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids","date":"2023-06-17","arxiv_id":"2306.10420","n_code_links":0,"syntology":null},{"paper":"/paper/multiwave-multiresolution-deep-architectures","slug":"multiwave-multiresolution-deep-architectures","title":"MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction","date":"2023-06-16","arxiv_id":"2306.10164","n_code_links":1,"syntology":null},{"paper":null,"slug":"systematic-architectural-design-of-scale","title":"Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis","date":"2023-06-16","arxiv_id":"2306.10128","n_code_links":0,"syntology":null},{"paper":"/paper/a-self-supervised-miniature-one-shot-texture","slug":"a-self-supervised-miniature-one-shot-texture","title":"A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model for Real-Time Robot Navigation and Embedded Applications","date":"2023-06-15","arxiv_id":"2306.08814","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-policy-gradient-methods-in-commodity","title":"Deep Policy Gradient Methods in Commodity Markets","date":"2023-06-14","arxiv_id":"2308.01910","n_code_links":0,"syntology":null},{"paper":null,"slug":"em-network-oracle-guided-self-distillation","title":"EM-Network: Oracle Guided Self-distillation for Sequence Learning","date":"2023-06-14","arxiv_id":"2306.10058","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-target-backdoor-attacks-for-code-pre","title":"Multi-target Backdoor Attacks for Code Pre-trained Models","date":"2023-06-14","arxiv_id":"2306.08350","n_code_links":0,"syntology":null},{"paper":null,"slug":"recipes-for-sequential-pre-training-of","title":"Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models","date":"2023-06-14","arxiv_id":"2306.08756","n_code_links":0,"syntology":null},{"paper":"/paper/t5-sr-a-unified-seq-to-seq-decoding-strategy","slug":"t5-sr-a-unified-seq-to-seq-decoding-strategy","title":"T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing","date":"2023-06-14","arxiv_id":"2306.08368","n_code_links":1,"syntology":null},{"paper":"/paper/the-elm-neuron-an-efficient-and-expressive","slug":"the-elm-neuron-an-efficient-and-expressive","title":"The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks","date":"2023-06-14","arxiv_id":"2306.16922","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":3,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["AaronSpieler/elmneuron"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"adversarial-capsule-networks-for-romanian","title":"Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis","date":"2023-06-13","arxiv_id":"2306.07845","n_code_links":0,"syntology":null},{"paper":null,"slug":"require-process-control-textbf-lstmc-is-all","title":"Require Process Control? LSTMc is all you need!","date":"2023-06-13","arxiv_id":"2306.07510","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-for-background-replacement-in","slug":"deep-learning-for-background-replacement-in","title":"Deep learning for Background Replacement in Video Conferencing","date":"2023-06-12","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/izindaba-tindzaba-machine-learning-news","slug":"izindaba-tindzaba-machine-learning-news","title":"Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati","date":"2023-06-12","arxiv_id":"2306.07426","n_code_links":1,"syntology":null},{"paper":null,"slug":"personality-trait-classification-using-cnn","title":"Personality Trait Classification Using CNN-LSTM Model","date":"2023-06-11","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"semantically-aware-mask-cyclegan-for","title":"Semantically-aware Mask CycleGAN for Translating Artistic Portraits to Photo-realistic Visualizations","date":"2023-06-11","arxiv_id":"2306.06577","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-neural-network-compression-via","title":"End-to-End Neural Network Compression via $\\frac{\\ell_1}{\\ell_2}$ Regularized Latency Surrogates","date":"2023-06-09","arxiv_id":"2306.05785","n_code_links":0,"syntology":null},{"paper":"/paper/everybody-compose-deep-beats-to-music","slug":"everybody-compose-deep-beats-to-music","title":"Everybody Compose: Deep Beats To Music","date":"2023-06-09","arxiv_id":"2306.06284","n_code_links":1,"syntology":null},{"paper":"/paper/prodigy-an-expeditiously-adaptive-parameter","slug":"prodigy-an-expeditiously-adaptive-parameter","title":"Prodigy: An Expeditiously Adaptive Parameter-Free Learner","date":"2023-06-09","arxiv_id":"2306.06101","n_code_links":1,"syntology":null},{"paper":null,"slug":"public-transit-demand-prediction-during","title":"Share, Collaborate, Benchmark: Advancing Travel Demand Research through rigorous open-source collaboration","date":"2023-06-09","arxiv_id":"2306.06194","n_code_links":0,"syntology":null},{"paper":null,"slug":"rate-forecaster-based-energy-aware-band","title":"Rate Forecaster based Energy Aware Band Assignment in Multiband Networks","date":"2023-06-08","arxiv_id":"2306.05369","n_code_links":0,"syntology":null},{"paper":null,"slug":"sequence-to-sequence-model-with-transformer","title":"Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring","date":"2023-06-08","arxiv_id":"2306.05012","n_code_links":0,"syntology":null},{"paper":"/paper/revising-deep-learning-methods-in-parking-lot","slug":"revising-deep-learning-methods-in-parking-lot","title":"Revising deep learning methods in parking lot occupancy detection","date":"2023-06-07","arxiv_id":"2306.04288","n_code_links":1,"syntology":null},{"paper":null,"slug":"unpaired-deep-learning-for-pharmacokinetic","title":"Unpaired Deep Learning for Pharmacokinetic Parameter Estimation from Dynamic Contrast-Enhanced MRI","date":"2023-06-07","arxiv_id":"2306.04339","n_code_links":0,"syntology":null},{"paper":null,"slug":"forecasting-the-performance-of-us-stock","title":"Forecasting the Performance of US Stock Market Indices During COVID-19: RF vs LSTM","date":"2023-06-06","arxiv_id":"2306.03620","n_code_links":0,"syntology":null},{"paper":null,"slug":"rdfc-gan-rgb-depth-fusion-cyclegan-for-indoor","title":"RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion","date":"2023-06-06","arxiv_id":"2306.03584","n_code_links":0,"syntology":null},{"paper":"/paper/towards-resilient-and-secure-smart-grids","slug":"towards-resilient-and-secure-smart-grids","title":"Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach","date":"2023-06-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-vessel-segmentation-based-cyclegan-for","title":"A Vessel-Segmentation-Based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis","date":"2023-06-05","arxiv_id":"2306.02901","n_code_links":0,"syntology":null}],"record_sha256":"46711fc531fc0edbfb00ececb4266aae4bdf3679320025442af5ec055a45e789","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}