{"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/tanh-activation/papers/41","list_of":"/method/tanh-activation","method":"Tanh 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":41,"pages_in_order":64,"rows_per_page":100,"rows":[4001,4100],"of":6333,"counts":{"archive_papers_tagged":6333,"with_a_code_link":2134,"where_syntology_ran_a_sample":386,"not_listed_spam_title":0,"listed":6333,"listed_where_code_ran":386,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":324,"every_run_a_failure_of_syntologys_instrument":62,"listed_with_a_run_with_no_instrument_failure":324,"listed_every_run_a_failure_of_syntologys_instrument":62,"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/tanh-activation","prev":"/method/tanh-activation/papers/40","next":"/method/tanh-activation/papers/42","papers":[{"paper":"/paper/fastai-a-layered-api-for-deep-learning","slug":"fastai-a-layered-api-for-deep-learning","title":"fastai: A Layered API for Deep Learning","date":"2020-02-11","arxiv_id":"2002.04688","n_code_links":2,"syntology":{"ran":1,"of":8,"n_ran_checked":1,"n_instrument":0,"unverified":7,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","official":{"repos":["fastai/fastai"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"paper":"/paper/to-share-or-not-to-share-a-comprehensive","slug":"to-share-or-not-to-share-a-comprehensive","title":"To Share or Not To Share: A Comprehensive Appraisal of Weight-Sharing","date":"2020-02-11","arxiv_id":"2002.04289","n_code_links":1,"syntology":{"ran":7,"of":7,"n_ran_checked":7,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["apourchot/to_share_or_not_to_share"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-spike-in-performance-training-hybrid","title":"A Spike in Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation Functions","date":"2020-02-10","arxiv_id":"2002.03553","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-sequence-to-sequence-learning","slug":"evaluating-sequence-to-sequence-learning","title":"Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis","date":"2020-02-10","arxiv_id":"2002.03485","n_code_links":1,"syntology":null},{"paper":null,"slug":"localized-flood-detectionwith-minimal-labeled","title":"Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning","date":"2020-02-10","arxiv_id":"2003.04973","n_code_links":0,"syntology":null},{"paper":"/paper/hhh-an-online-medical-chatbot-system-based-on-1","slug":"hhh-an-online-medical-chatbot-system-based-on-1","title":"HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention","date":"2020-02-08","arxiv_id":"2002.03140","n_code_links":2,"syntology":null},{"paper":null,"slug":"on-a-scalable-entropic-breaching-of-the","title":"On a scalable entropic breaching of the overfitting barrier in machine learning","date":"2020-02-08","arxiv_id":"2002.03176","n_code_links":0,"syntology":null},{"paper":"/paper/finitenet-a-fully-convolutional-lstm-network","slug":"finitenet-a-fully-convolutional-lstm-network","title":"FiniteNet: A Fully Convolutional LSTM Network Architecture for Time-Dependent Partial Differential Equations","date":"2020-02-07","arxiv_id":"2002.03014","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["FiniteNetICML2020Code/FiniteNet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"high-temporal-resolution-rainfall-runoff","title":"High Temporal Resolution Rainfall Runoff Modelling Using Long-Short-Term-Memory (LSTM) Networks","date":"2020-02-07","arxiv_id":"2002.02568","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-adaptive-lstm-network-for-real-time","title":"Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation","date":"2020-02-07","arxiv_id":"2002.02598","n_code_links":0,"syntology":null},{"paper":null,"slug":"compositional-neural-machine-translation-by","title":"Compositional Neural Machine Translation by Removing the Lexicon from Syntax","date":"2020-02-06","arxiv_id":"2002.08899","n_code_links":0,"syntology":null},{"paper":null,"slug":"deepbrain-towards-personalized-eeg","title":"DeepBrain: Towards Personalized EEG Interaction through Attentional and Embedded LSTM Learning","date":"2020-02-06","arxiv_id":"2002.02086","n_code_links":0,"syntology":null},{"paper":"/paper/driver-gaze-estimation-in-the-real-world","slug":"driver-gaze-estimation-in-the-real-world","title":"Gaze Preserving CycleGANs for Eyeglass Removal & Persistent Gaze Estimation","date":"2020-02-06","arxiv_id":"2002.02077","n_code_links":1,"syntology":null},{"paper":"/paper/variational-depth-search-in-resnets","slug":"variational-depth-search-in-resnets","title":"Variational Depth Search in ResNets","date":"2020-02-06","arxiv_id":"2002.02797","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["cambridge-mlg/arch_uncert"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/level-three-synthetic-fingerprint-generation","slug":"level-three-synthetic-fingerprint-generation","title":"Level Three Synthetic Fingerprint Generation","date":"2020-02-05","arxiv_id":"2002.03809","n_code_links":2,"syntology":null},{"paper":null,"slug":"improving-efficiency-in-large-scale","title":"Improving Efficiency in Large-Scale Decentralized Distributed Training","date":"2020-02-04","arxiv_id":"2002.01119","n_code_links":0,"syntology":null},{"paper":"/paper/syntactically-look-ahead-attention-network","slug":"syntactically-look-ahead-attention-network","title":"Syntactically Look-Ahead Attention Network for Sentence Compression","date":"2020-02-04","arxiv_id":"2002.01145","n_code_links":1,"syntology":null},{"paper":"/paper/learning-contextualized-document","slug":"learning-contextualized-document","title":"Learning Contextualized Document Representations for Healthcare Answer Retrieval","date":"2020-02-03","arxiv_id":"2002.00835","n_code_links":1,"syntology":null},{"paper":null,"slug":"prophet-proactive-candidate-selection-for","title":"Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases","date":"2020-02-03","arxiv_id":"2002.00577","n_code_links":0,"syntology":null},{"paper":"/paper/interpreting-video-features-a-comparison-of-1","slug":"interpreting-video-features-a-comparison-of-1","title":"Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks","date":"2020-02-02","arxiv_id":"2002.00367","n_code_links":2,"syntology":null},{"paper":null,"slug":"model-extraction-attacks-against-recurrent","title":"Model Extraction Attacks against Recurrent Neural Networks","date":"2020-02-01","arxiv_id":"2002.00123","n_code_links":0,"syntology":null},{"paper":"/paper/transforming-spectrum-and-prosody-for","slug":"transforming-spectrum-and-prosody-for","title":"Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training Data","date":"2020-02-01","arxiv_id":"2002.00198","n_code_links":1,"syntology":null},{"paper":null,"slug":"compensation-of-fiber-nonlinearities-in","title":"Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks","date":"2020-01-31","arxiv_id":"2001.11802","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-emotion-primitives-from-speech-and","title":"Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions","date":"2020-01-31","arxiv_id":"2002.01323","n_code_links":0,"syntology":null},{"paper":null,"slug":"gating-creates-slow-modes-and-controls-phase","title":"Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs","date":"2020-01-31","arxiv_id":"2002.00025","n_code_links":0,"syntology":null},{"paper":null,"slug":"pseudo-bidirectional-decoding-for-local","title":"Pseudo-Bidirectional Decoding for Local Sequence Transduction","date":"2020-01-31","arxiv_id":"2001.11694","n_code_links":0,"syntology":null},{"paper":null,"slug":"dual-convolutional-lstm-network-for-referring","title":"Dual Convolutional LSTM Network for Referring Image Segmentation","date":"2020-01-30","arxiv_id":"2001.11561","n_code_links":0,"syntology":null},{"paper":null,"slug":"audio-visual-decision-fusion-for-wfst-based","title":"Audio-Visual Decision Fusion for WFST-based and seq2seq Models","date":"2020-01-29","arxiv_id":"2001.10832","n_code_links":0,"syntology":null},{"paper":null,"slug":"human-action-performance-using-deep-neuro","title":"Human Action Performance using Deep Neuro-Fuzzy Recurrent Attention Model","date":"2020-01-29","arxiv_id":"2001.10953","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-contextual-modeling-for-asr-correction","title":"Joint Contextual Modeling for ASR Correction and Language Understanding","date":"2020-01-28","arxiv_id":"2002.00750","n_code_links":0,"syntology":null},{"paper":"/paper/nas-bench-1shot1-benchmarking-and-dissecting-1","slug":"nas-bench-1shot1-benchmarking-and-dissecting-1","title":"NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search","date":"2020-01-28","arxiv_id":"2001.10422","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-program-synthesis-for-images","title":"Unsupervised Program Synthesis for Images By Sampling Without Replacement","date":"2020-01-27","arxiv_id":"2001.10119","n_code_links":0,"syntology":null},{"paper":"/paper/intent-classification-in-question-answering","slug":"intent-classification-in-question-answering","title":"Intent Classification in Question-Answering Using LSTM Architectures","date":"2020-01-25","arxiv_id":"2001.09330","n_code_links":1,"syntology":null},{"paper":null,"slug":"compressing-language-models-using-doped","title":"Compressing Language Models using Doped Kronecker Products","date":"2020-01-24","arxiv_id":"2001.08896","n_code_links":0,"syntology":null},{"paper":null,"slug":"dalc-distributed-automatic-lstm-customization","title":"DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction","date":"2020-01-24","arxiv_id":"2001.09821","n_code_links":0,"syntology":null},{"paper":null,"slug":"kernel-of-cyclegan-as-a-principle-homogeneous","title":"Kernel of CycleGAN as a Principle homogeneous space","date":"2020-01-24","arxiv_id":"2001.09061","n_code_links":0,"syntology":null},{"paper":null,"slug":"applying-recent-innovations-from-nlp-to-mooc","title":"Applying Recent Innovations from NLP to MOOC Student Course Trajectory Modeling","date":"2020-01-23","arxiv_id":"2001.08333","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-complexity-lstm-training-and-inference","title":"Low-Complexity LSTM Training and Inference with FloatSD8 Weight Representation","date":"2020-01-23","arxiv_id":"2001.08450","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-objective-neural-architecture-search-2","title":"Multi-objective Neural Architecture Search via Non-stationary Policy Gradient","date":"2020-01-23","arxiv_id":"2001.08437","n_code_links":0,"syntology":null},{"paper":"/paper/attention-a-lightweight-2d-hand-pose","slug":"attention-a-lightweight-2d-hand-pose","title":"Attention! A Lightweight 2D Hand Pose Estimation Approach","date":"2020-01-22","arxiv_id":"2001.08047","n_code_links":1,"syntology":null},{"paper":"/paper/graphgen-a-scalable-approach-to-domain","slug":"graphgen-a-scalable-approach-to-domain","title":"GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation","date":"2020-01-22","arxiv_id":"2001.08184","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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) · 0 unverified","official":{"repos":["idea-iitd/graphgen"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"transition-based-dependency-parsing-using","title":"Transition-Based Dependency Parsing using Perceptron Learner","date":"2020-01-22","arxiv_id":"2001.08279","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-hybrid-model-based-on-deep-lstm-for","title":"A hybrid model based on deep LSTM for predicting high-dimensional chaotic systems","date":"2020-01-21","arxiv_id":"2002.00799","n_code_links":0,"syntology":null},{"paper":"/paper/single-headed-attention-based-sequence-to","slug":"single-headed-attention-based-sequence-to","title":"Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard","date":"2020-01-20","arxiv_id":"2001.07263","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-for-hindi-text-classification-a","title":"Deep Learning for Hindi Text Classification: A Comparison","date":"2020-01-19","arxiv_id":"2001.10340","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-neural-architecture-search-a-broad","title":"BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture","date":"2020-01-18","arxiv_id":"2001.06679","n_code_links":0,"syntology":null},{"paper":null,"slug":"enas-u-net-evolutionary-neural-architecture","title":"Evolutionary Neural Architecture Search for Retinal Vessel Segmentation","date":"2020-01-18","arxiv_id":"2001.06678","n_code_links":0,"syntology":null},{"paper":"/paper/compounding-the-performance-improvements-of","slug":"compounding-the-performance-improvements-of","title":"Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network","date":"2020-01-17","arxiv_id":"2001.06268","n_code_links":1,"syntology":{"ran":3,"of":11,"n_ran_checked":2,"n_instrument":1,"unverified":8,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","official":{"repos":["clovaai/assembled-cnn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":8,"ran_from_kinds":["official"]}}},{"paper":"/paper/latency-aware-differentiable-neural","slug":"latency-aware-differentiable-neural","title":"Latency-Aware Differentiable Neural Architecture Search","date":"2020-01-17","arxiv_id":"2001.06392","n_code_links":1,"syntology":null},{"paper":null,"slug":"up-to-two-billion-times-acceleration-of","title":"Building high accuracy emulators for scientific simulations with deep neural architecture search","date":"2020-01-17","arxiv_id":"2001.08055","n_code_links":0,"syntology":null},{"paper":null,"slug":"fact-aware-sentence-split-and-rephrase-with","title":"Fact-aware Sentence Split and Rephrase with Permutation Invariant Training","date":"2020-01-16","arxiv_id":"2001.11383","n_code_links":0,"syntology":null},{"paper":"/paper/mixpath-a-unified-approach-for-one-shot","slug":"mixpath-a-unified-approach-for-one-shot","title":"MixPath: A Unified Approach for One-shot Neural Architecture Search","date":"2020-01-16","arxiv_id":"2001.05887","n_code_links":1,"syntology":{"ran":0,"of":4,"n_ran_checked":0,"n_instrument":0,"unverified":4,"pointer_only":4,"phrase":"0 ran · 4 unverified","official":{"repos":["xiaomi-automl/MixPath"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":[]}}},{"paper":"/paper/predictive-analysis-of-bitcoin-price","slug":"predictive-analysis-of-bitcoin-price","title":"Predictive analysis of Bitcoin price considering social sentiments","date":"2020-01-16","arxiv_id":"2001.10343","n_code_links":1,"syntology":null},{"paper":null,"slug":"stream-flow-forecasting-of-small-rivers-based","title":"Stream-Flow Forecasting of Small Rivers Based on LSTM","date":"2020-01-16","arxiv_id":"2001.05681","n_code_links":0,"syntology":null},{"paper":"/paper/aggressionnet-generalised-multi-modal-deep","slug":"aggressionnet-generalised-multi-modal-deep","title":"A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts","date":"2020-01-15","arxiv_id":"2001.05493","n_code_links":0,"syntology":null},{"paper":"/paper/cdgan-cyclic-discriminative-generative","slug":"cdgan-cyclic-discriminative-generative","title":"CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation","date":"2020-01-15","arxiv_id":"2001.05489","n_code_links":1,"syntology":null},{"paper":"/paper/neural-architecture-search-for-deep-image","slug":"neural-architecture-search-for-deep-image","title":"Neural Architecture Search for Deep Image Prior","date":"2020-01-14","arxiv_id":"2001.04776","n_code_links":2,"syntology":null},{"paper":"/paper/physical-virtual-collaboration-graph-network","slug":"physical-virtual-collaboration-graph-network","title":"Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction","date":"2020-01-14","arxiv_id":"2001.04889","n_code_links":2,"syntology":null},{"paper":"/paper/adabert-task-adaptive-bert-compression-with","slug":"adabert-task-adaptive-bert-compression-with","title":"AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search","date":"2020-01-13","arxiv_id":"2001.04246","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":null}},{"paper":"/paper/gridmask-data-augmentation","slug":"gridmask-data-augmentation","title":"GridMask Data Augmentation","date":"2020-01-13","arxiv_id":"2001.04086","n_code_links":7,"syntology":null},{"paper":"/paper/visual-storytelling-via-predicting-anchor","slug":"visual-storytelling-via-predicting-anchor","title":"Visual Storytelling via Predicting Anchor Word Embeddings in the Stories","date":"2020-01-13","arxiv_id":"2001.04541","n_code_links":0,"syntology":null},{"paper":null,"slug":"urdu-english-machine-transliteration-using","title":"Urdu-English Machine Transliteration using Neural Networks","date":"2020-01-12","arxiv_id":"2001.05296","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-continuous-space-neural-language-model-for","title":"A Continuous Space Neural Language Model for Bengali Language","date":"2020-01-11","arxiv_id":"2001.05315","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-new-approach-for-trading-based-on-long","title":"A new approach for trading based on Long Short Term Memory technique","date":"2020-01-10","arxiv_id":"2001.03333","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-oriented-neural-architecture","title":"Performance-Oriented Neural Architecture Search","date":"2020-01-09","arxiv_id":"2001.02976","n_code_links":0,"syntology":null},{"paper":null,"slug":"dont-forget-the-past-recurrent-depth","title":"Don't Forget The Past: Recurrent Depth Estimation from Monocular Video","date":"2020-01-08","arxiv_id":"2001.02613","n_code_links":0,"syntology":null},{"paper":"/paper/fast-neural-network-adaptation-via-parameter","slug":"fast-neural-network-adaptation-via-parameter","title":"Fast Neural Network Adaptation via Parameter Remapping and Architecture Search","date":"2020-01-08","arxiv_id":"2001.02525","n_code_links":0,"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":"high-level-plan-for-behavioral-robot","title":"High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET","date":"2020-01-08","arxiv_id":"2001.02330","n_code_links":0,"syntology":null},{"paper":null,"slug":"rmnv2-reduced-mobilenet-v2-for-cifar10","title":"RMNv2: Reduced Mobilenet V2 for CIFAR10","date":"2020-01-08","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/deeper-insights-into-weight-sharing-in-neural-1","slug":"deeper-insights-into-weight-sharing-in-neural-1","title":"Deeper Insights into Weight Sharing in Neural Architecture Search","date":"2020-01-06","arxiv_id":"2001.01431","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"4 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; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["ultmaster/deeper-insights-weight-sharing"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"macromolecule-classification-based-on-the","title":"Macromolecule Classification Based on the Amino-acid Sequence","date":"2020-01-06","arxiv_id":"2001.01717","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-business-process-structure","title":"Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study","date":"2020-01-05","arxiv_id":"2001.01243","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploiting-event-driven-cameras-for-spatio","title":"Exploiting Event Cameras for Spatio-Temporal Prediction of Fast-Changing Trajectories","date":"2020-01-05","arxiv_id":"2001.01248","n_code_links":0,"syntology":null},{"paper":null,"slug":"forecasting-bitcoin-closing-price-series","title":"Forecasting Bitcoin closing price series using linear regression and neural networks models","date":"2020-01-04","arxiv_id":"2001.01127","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-image-captioning-models-beyond","slug":"understanding-image-captioning-models-beyond","title":"Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning Models","date":"2020-01-04","arxiv_id":"2001.01037","n_code_links":1,"syntology":null},{"paper":"/paper/towards-automated-statistical-physics-data","slug":"towards-automated-statistical-physics-data","title":"Deep learning reveals hidden interactions in complex systems","date":"2020-01-03","arxiv_id":"2001.02539","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-deep-structural-model-for-analyzing","title":"A Deep Structural Model for Analyzing Correlated Multivariate Time Series","date":"2020-01-02","arxiv_id":"2001.00559","n_code_links":0,"syntology":null},{"paper":null,"slug":"bert-al-bert-for-arbitrarily-long-document","title":"BERT-AL: BERT for Arbitrarily Long Document Understanding","date":"2020-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"how-important-are-network-weights-to-what","title":"HOW IMPORTANT ARE NETWORK WEIGHTS? TO WHAT EXTENT DO THEY NEED AN UPDATE?","date":"2020-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"isbnet-instance-aware-selective-branching-1","title":"ISBNet: Instance-aware Selective Branching Networks","date":"2020-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"parallel-neural-text-to-speech-1","title":"Parallel Neural Text-to-Speech","date":"2020-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"transition-based-dependency-parser-for","title":"Transition Based Dependency Parser for Amharic Language Using Deep Learning","date":"2020-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-learning-of-automotive-3d-crash","title":"Unsupervised Learning of Automotive 3D Crash Simulations using LSTMs","date":"2020-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/connecting-optical-morphology-environment-and-1","slug":"connecting-optical-morphology-environment-and-1","title":"Connecting Optical Morphology, Environment, and H I Mass Fraction for Low-Redshift Galaxies Using Deep Learning","date":"2019-12-31","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"modeling-neural-architecture-search-methods","title":"Modeling Neural Architecture Search Methods for Deep Networks","date":"2019-12-31","arxiv_id":"1912.13183","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-modality-super-resolution-loss-for-gan","title":"Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database","date":"2019-12-30","arxiv_id":"1912.12838","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-architecture-search-on-acoustic-scene","title":"Neural Architecture Search on Acoustic Scene Classification","date":"2019-12-30","arxiv_id":"1912.12825","n_code_links":0,"syntology":null},{"paper":null,"slug":"grey-models-for-short-term-queue-length","title":"Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control","date":"2019-12-29","arxiv_id":"1912.12676","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-variational-imitation-learning","slug":"hierarchical-variational-imitation-learning","title":"Hierarchical Variational Imitation Learning of Control Programs","date":"2019-12-29","arxiv_id":"1912.12612","n_code_links":1,"syntology":null},{"paper":null,"slug":"natural-language-processing-of-mimic-iii","title":"Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks","date":"2019-12-28","arxiv_id":"1912.12397","n_code_links":0,"syntology":null},{"paper":"/paper/tha3aroon-at-nsurl-2019-task-8-semantic","slug":"tha3aroon-at-nsurl-2019-task-8-semantic","title":"Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic","date":"2019-12-28","arxiv_id":"1912.12514","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-multi-cascaded-model-with-data-augmentation","title":"A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts","date":"2019-12-27","arxiv_id":"1912.12068","n_code_links":0,"syntology":null},{"paper":"/paper/interpreting-lstm-prediction-on-solar-flare","slug":"interpreting-lstm-prediction-on-solar-flare","title":"Interpreting LSTM Prediction on Solar Flare Eruption with Time-series Clustering","date":"2019-12-27","arxiv_id":"1912.12360","n_code_links":1,"syntology":null},{"paper":null,"slug":"amharic-arabic-neural-machine-translation","title":"Amharic-Arabic Neural Machine Translation","date":"2019-12-26","arxiv_id":"1912.13161","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-resilience-of-deep-learning-for","title":"On the Resilience of Deep Learning for Reduced-voltage FPGAs","date":"2019-12-26","arxiv_id":"2001.00053","n_code_links":0,"syntology":null},{"paper":null,"slug":"utterance-level-permutation-invariant","title":"Utterance-level Permutation Invariant Training with Latency-controlled BLSTM for Single-channel Multi-talker Speech Separation","date":"2019-12-25","arxiv_id":"1912.11613","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-autoaugment-1","slug":"adversarial-autoaugment-1","title":"Adversarial AutoAugment","date":"2019-12-24","arxiv_id":"1912.11188","n_code_links":0,"syntology":null},{"paper":null,"slug":"betanas-balanced-training-and-selective-drop-1","title":"BETANAS: BalancEd TrAining and selective drop for Neural Architecture Search","date":"2019-12-24","arxiv_id":"1912.11191","n_code_links":0,"syntology":null},{"paper":"/paper/focusing-and-diffusion-bidirectional","slug":"focusing-and-diffusion-bidirectional","title":"Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition","date":"2019-12-24","arxiv_id":"1912.11521","n_code_links":0,"syntology":null},{"paper":"/paper/cnn-generated-images-are-surprisingly-easy-to","slug":"cnn-generated-images-are-surprisingly-easy-to","title":"CNN-generated images are surprisingly easy to spot... for now","date":"2019-12-23","arxiv_id":"1912.11035","n_code_links":5,"syntology":null}],"record_sha256":"0c0c1b5f80173b22cb109f52a4eea24d01854a16cc39ee72aac0d418a21ff732","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}