{"list":"other","total":67,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1310.4546","title":"Distributed Representations of Words and Phrases and their Compositionality","created":"2013-10-16","cited_by_count":3474},{"id":"2105.01601","title":"MLP-Mixer: An all-MLP Architecture for Vision","created":"2021-05-04","cited_by_count":1446},{"id":"1906.02243","title":"Energy and Policy Considerations for Deep Learning in NLP","created":"2019-06-05","cited_by_count":1254},{"id":"2008.08186","title":"Prevalence of Neural Collapse during the terminal phase of deep learning training","created":"2020-08-18","cited_by_count":719},{"id":"1410.5401","title":"Neural Turing Machines","created":"2014-10-20","cited_by_count":644},{"id":"2008.02217","title":"Hopfield Networks is All You Need","created":"2020-07-16","cited_by_count":475},{"id":"1810.07758","title":"The UCR Time Series Archive","created":"2018-10-17","cited_by_count":431},{"id":"2011.15091","title":"Inductive Biases for Deep Learning of Higher-Level Cognition","created":"2020-11-30","cited_by_count":275},{"id":"2012.05876","title":"Neurosymbolic AI: The 3rd Wave","created":"2020-12-10","cited_by_count":255},{"id":"1802.00543","title":"Modeling polypharmacy side effects with graph convolutional networks","created":"2018-02-02","cited_by_count":237},{"id":"2012.05208","title":"On the Binding Problem in Artificial Neural Networks","created":"2020-12-09","cited_by_count":233},{"id":"2002.06177","title":"The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence","created":"2020-02-14","cited_by_count":195},{"id":"1905.06088","title":"Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning","created":"2019-05-15","cited_by_count":151},{"id":"2102.12627","title":"How to represent part-whole hierarchies in a neural network","created":"2021-02-25","cited_by_count":123},{"id":"2111.06077","title":"A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations","created":"2021-11-11","cited_by_count":99},{"id":"2003.04821","title":"Benchmarking TinyML Systems: Challenges and Direction","created":"2020-03-10","cited_by_count":91},{"id":"1902.05522","title":"Superposition of many models into one","created":"2019-02-14","cited_by_count":64},{"id":"2001.11797","title":"A comparison of Vector Symbolic Architectures","created":"2020-01-31","cited_by_count":64},{"id":"2004.11204","title":"Classification using Hyperdimensional Computing: A Review","created":"2020-04-19","cited_by_count":60},{"id":"2109.03429","title":"Computing on Functions Using Randomized Vector Representations","created":"2021-09-08","cited_by_count":41},{"id":"1602.03032","title":"Associative Long Short-Term Memory","created":"2016-02-09","cited_by_count":37},{"id":"2106.05268","title":"Vector Symbolic Architectures as a Computing Framework for Emerging Hardware","created":"2021-06-09","cited_by_count":35},{"id":"2101.07720","title":"Hyperdimensional computing as a framework for systematic aggregation of image descriptors","created":"2021-01-19","cited_by_count":32},{"id":"2010.01939","title":"Robust High-dimensional Memory-augmented Neural Networks","created":"2020-10-05","cited_by_count":31},{"id":"1803.00412","title":"A theory of sequence indexing and working memory in recurrent neural networks","created":"2018-02-28","cited_by_count":29},{"id":"2111.05498","title":"Attention Approximates Sparse Distributed Memory","created":"2021-11-10","cited_by_count":29},{"id":"2009.06734","title":"Variable Binding for Sparse Distributed Representations: Theory and Applications","created":"2020-09-14","cited_by_count":27},{"id":"1812.08718","title":"RNNs Implicitly Implement Tensor Product Representations","created":"2018-12-20","cited_by_count":26},{"id":"2109.02157","title":"Learning with Holographic Reduced Representations","created":"2021-09-05","cited_by_count":25},{"id":"1705.07199","title":"The High-Dimensional Geometry of Binary Neural Networks","created":"2017-05-19","cited_by_count":22},{"id":"1901.07718","title":"Robust computation with rhythmic spike patterns","created":"2019-01-23","cited_by_count":21},{"id":"2103.06709","title":"Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices","created":"2021-03-08","cited_by_count":16},{"id":"1909.09153","title":"Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks","created":"2019-09-19","cited_by_count":15},{"id":"1809.01926","title":"One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing","created":"2018-09-06","cited_by_count":14},{"id":"1802.10237","title":"An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier","created":"2018-02-28","cited_by_count":13},{"id":"2010.05223","title":"End to End Binarized Neural Networks for Text Classification","created":"2020-10-11","cited_by_count":13},{"id":"1705.03934","title":"Autoscaling Bloom Filter: Controlling Trade-off Between True and False Positives","created":"2017-05-10","cited_by_count":12},{"id":"1709.00848","title":"Neural Distributed Autoassociative Memories: A Survey","created":"2017-09-04","cited_by_count":12},{"id":"2004.12691","title":"Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs","created":"2020-04-27","cited_by_count":12},{"id":"2106.09821","title":"Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing","created":"2021-06-17","cited_by_count":12},{"id":"1706.00280","title":"Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware","created":"2017-06-01","cited_by_count":11},{"id":"1804.09123","title":"PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform","created":"2018-04-24","cited_by_count":11},{"id":"1905.12730","title":"Recursive Sketches for Modular Deep Learning","created":"2019-05-29","cited_by_count":11},{"id":"2003.01821","title":"HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics","created":"2020-03-03","cited_by_count":11},{"id":"2105.00934","title":"Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection","created":"2021-05-03","cited_by_count":9},{"id":"2103.05267","title":"Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing","created":"2021-03-09","cited_by_count":8},{"id":"2110.08343","title":"Hyperseed: Unsupervised Learning with Vector Symbolic Architectures","created":"2021-10-15","cited_by_count":8},{"id":"2003.11458","title":"Commentaries on \"Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception\" [Science Robotics Vol. 4 Issue 30 (2019) 1-10","created":"2020-03-25","cited_by_count":7},{"id":"2106.00881","title":"Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks","created":"2021-06-02","cited_by_count":7},{"id":"2106.12029","title":"MIMHD: Accurate and Efficient Hyperdimensional Inference Using Multi-Bit In-Memory Computing","created":"2021-06-22","cited_by_count":7},{"id":"1812.05705","title":"Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces","created":"2018-12-13","cited_by_count":6},{"id":"2102.02758","title":"A 5 \\mu W Standard Cell Memory-based Configurable Hyperdimensional Computing Accelerator for Always-on Smart Sensing","created":"2021-02-04","cited_by_count":6},{"id":"1806.10755","title":"A Computational Theory for Life-Long Learning of Semantics","created":"2018-06-28","cited_by_count":5},{"id":"2004.14252","title":"Task-Projected Hyperdimensional Computing for Multi-Task Learning","created":"2020-04-29","cited_by_count":5},{"id":"2007.15222","title":"SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning","created":"2020-07-30","cited_by_count":5},{"id":"1711.10185","title":"Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end","created":"2017-11-28","cited_by_count":4},{"id":"2003.05171","title":"Vector symbolic architectures for context-free grammars","created":"2020-03-11","cited_by_count":3},{"id":"2010.00084","title":"The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations","created":"2020-09-30","cited_by_count":3},{"id":"2104.06555","title":"Should Semantic Vector Composition be Explicit? Can it be Linear?","created":"2021-04-13","cited_by_count":3},{"id":"2106.02894","title":"MoleHD: Ultra-Low-Cost Drug Discovery using Hyperdimensional Computing","created":"2021-06-05","cited_by_count":3},{"id":"2106.08446","title":"Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations","created":"2021-06-15","cited_by_count":3},{"id":"2104.00424","title":"High-dimensional distributed semantic spaces for utterances","created":"2021-04-01","cited_by_count":2},{"id":"2104.02804","title":"Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata","created":"2021-04-06","cited_by_count":2},{"id":"2106.09831","title":"On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks","created":"2021-06-17","cited_by_count":2},{"id":"2010.07004","title":"Binarization Methods for Motor-Imagery Brain-Computer Interface Classification","created":"2020-10-14","cited_by_count":1},{"id":"2110.09166","title":"Branch Predicting with Sparse Distributed Memories","created":"2021-10-18","cited_by_count":1}],"next_cursor":null}