NeurIPSW 2019

121 papers

$C^\infty$ Smooth Algorithmic Neural Networks for Solving Inverse Problems Felix Petersen, Christian Borgelt, Oliver Deussen

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A GAN Based Solver of Black-Box Inverse Problems Michael Gillhofer, Hubert Ramsauer, Johannes Brandstetter, Bernhard Schäfl, Sepp Hochreiter

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A Hybrid Architecture for On-Device Compressive Machine Learning Yang Li, Thomas Strohmer

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Ad-Hoc Bayesian Program Learning Eli Sennesh

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Additive Function Approximation in the Brain Kameron Decker Harris

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Adversarial Training of Neural Encoding Models on Population Spike Trains Poornima Ramesh, Mohamad Atayi, Jakob H Macke

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Applications of a Disintegration Transformation Praveen Narayanan, Chung-chieh Shan

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Approximations in Probabilistic Programs Ekansh Sharma, Daniel M. Roy

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Are Skip Connections Necessary for Biologically Plausible Learning Rules? Daniel Jiwoong Im, Rutuja Patil, Kristin Branson

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Augmenting Supervised Learning by Meta-Learning Unsupervised Local Rules Jeffrey Siedar Cheng, Ari Benjamin, Benjamin Lansdell, Konrad Paul Kording

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Auto-Encoders for Compressed Sensing Pei Peng, Shirin Jalali, Xin Yuan

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Automatic Differentiation: Inverse Accumulation Mode Jeffrey Mark Siskind

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BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding Emad Elwany, Dave Moore, Gaurav Oberoi

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BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding Timo I. Denk, Christian Reisswig

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Biologically Plausible Neural Networks via Evolutionary Dynamics and Dopaminergic Plasticity Sruthi Gorantla, Anand Louis, Christos H. Papadimitriou, Santosh Vempala, Naganand Yadati

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Biologically-Inspired Spatial Neural Networks Maciej Wołczyk, Jacek Tabor, Marek Śmieja, Szymon Maszke

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Brain-Inspired Robust Vision Using Convolutional Neural Networks with Feedback Yujia Huang, Sihui Dai, Tan Nguyen, Pinglei Bao, Doris Y. Tsao, Richard G. Baraniuk, Anima Anandkumar

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Cellular Neuromodulation in Artificial Networks Vecoven Nicolas, Ernst Damien, Wehenkel Antoine, Drion Guillaume

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Chargrid-OCR: End-to-End Trainable Optical Character Recognition Through Semantic Segmentation and Object Detection Christian Reisswig, Anoop R Katti, Marco Spinaci, Johannes Höhne

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Checking Functional Modularity in DNN by Biclustering Task-Specific Hidden Neurons Jialin Lu, Martin Ester

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Co-Generation with GANs Using AIS Based HMC Tiantian Fang, Alexander G. Schwing

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Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro Du Phan, Neeraj Pradhan, Martin Jankowiak

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Compressed Sensing and Overparametrized Networks: Overfitting Peaks in a Model of Misparametrized Sparse Regression in the Interpolation Limit Partha P Mitra

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Continual Learning via Neural Pruning Siavash Golkar, Micheal Kagan, Kyunghyun Cho

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Convolutional Neural Networks with Extra-Classical Receptive Fields Brian Hu, Ramakrishnan Iyer, Stefan Mihalas

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Convolutionary, Evolutionary, Revolutionary: What’s Next for Bodies, Brains and AI? Peter Stratton

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Coordinate-VAE: Unsupervised Clustering and De-Noising of Peripheral Nervous System Data Thomas J Hardcastle, Susannah Lee, Lorenz Wernisch, Pascal Fortier-Poisson, Sudha Shunmugam, Kalon Hewage, Tris Edwards, Oliver Armitage, Emil Hewage

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CORD: A Consolidated Receipt Dataset for Post-OCR Parsing Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jaeheung Surh, Minjoon Seo, Hwalsuk Lee

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CrossLang: The System of Cross-Lingual Plagiarism Detection Oleg Bakhteev, Alexandr Ogaltsov, Andrey Khazov, Kamil Safin, Rita Kuznetsova

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Data-Driven Discovery of Functional Cell Types That Improve Models of Neural Activity Daniel Zdeblick, Eric Shea-Brown, Daniela Witten, Michael Buice

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Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks Nicholas Roberts, Dian Ang Yap, Vinay Uday Prabhu

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DeepErase: Weakly Supervised Ink Artifact Removal in Document Text Images Yike Qi, W. Ronny Huang, Qianqian Li, Jonathan L. Degange

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Dex: Array Programming with Typed Indices Dougal Maclaurin, Alexey Radul, Matthew J. Johnson, and Dimitrios Vytiniotis

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Differentiation of High-Level Language Semantics Michael Innes

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Disentangling the Roles of Dimensionality and Cell Classes in Neural Computations Alexis M Dubreuil, Adrian Valente, Francesca Mastrogiuseppe, Srdjan Ostojic

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Doc2Dial: A Framework for Dialogue Composition Grounded in Business Documents Song Feng, Kshitij Fadni, Q. Vera Liao, Luis A. Lastras

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Document Enhancement System Using Auto-Encoders Mehrdad J. Gangeh, Sunil R. Tiyyagura, Sridhar V. Dasaratha, Hamid Motahari, Nigel P. Duffy

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Does the Neuronal Noise in Cortex Help Generalization? Brian Hu, Jiaqi Shang, Ramakrishnan Iyer, Josh Siegle, Stefan Mihalas

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Eligibility Traces Provide a Data-Inspired Alternative to Backpropagation Through Time Guillaume Bellec, Franz Scherr, Elias Hajek, Darjan Salaj, Anand Subramoney, Robert Legenstein, Wolfgang Maass

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Emergent Structures and Lifetime Structure Evolution in Artificial Neural Networks Siavash Golkar

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Energy Dissipation with Plug-and-Play Priors Hendrik Sommerhoff, Andreas Kolb, Michael Moeller

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Estimating Encoding Models of Cortical Auditory Processing Using Naturalistic Stimuli and Transfer Learning Nicolas Farrugia, Victor Nepveu, Deycy Camila Arias Villamil

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Evaluating Biological Plausibility of Learning Algorithms the Lazy Way Owen Marschall, Kyunghyun Cho, Cristina Savin

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Evolving the Olfactory System Robert Guangyu Yang, Peter Yiliu Wang, Yi Sun, Ashok Litwin-Kumar, Richard Axel, Lf Abbott

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Exploring Properties of the Deep Image Prior Andreas Kattamis, Tameem Adel, Adrian Weller

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Extreme Few-View CT Reconstruction Using Deep Inference Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley

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Flexible Degrees of Connectivity Under Synaptic Weight Constraints Gabriel Koch Ocker, Michael A. Buice

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From Stroke to Finite Automata: An Offline Recognition Approach Kehinde Aruleba

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Functional Annotation of Human Cognitive States Using Graph Convolution Networks Yu Zhang, Pierre Bellec

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Functional Tensors for Probabilistic Programming Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Du Phan, Jonathan Chen

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GAN Priors for Bayesian Inference Dhruv V. Patel, Assad A. Oberai

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Generalized Abs-Linear Learning Andreas Griewank, Ángel Rojas

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Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling Xiaoyang Rebecca Li, Nikolaos Mitsakos, Ping Lu, Yuan Xiao, Xing Zhao

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Generative Models for Low-Dimensional Video Representation and Compressive Sensing Rakib Hyder, M. Salman Asif

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Gradient-Based Neural DAG Learning Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien

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How Well Do Deep Neural Networks Trained on Object Recognition Characterize the Mouse Visual System? Santiago A. Cadena, Fabian H. Sinz, Taliah Muhammad, Emmanouil Froudarakis, Erick Cobos, Edgar Y. Walker, Jake Reimer, Matthias Bethge, Andreas Tolias, Alexander S. Ecker

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Improving Limited Angle CT Reconstruction with a Robust GAN Prior Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle Champley

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Inferring Hierarchies of Latent Features in Calcium Imaging Data Luke Y. Prince, Blake A. Richards

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Information Extraction from Text Regions with Complex Tabular Structure Kaixuan Zhang, Zejiang Shen, Jie Zhou, Melissa Dell

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Insect Cyborgs: Bio-Mimetic Feature Generators Improve ML Accuracy on Limited Data Charles B. Delahunt, J. Nathan Kutz

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Interpretable and Robust Blind Image Denoising with Bias-Free Convolutional Neural Networks Zahra Kadkhodaie, Sreyas Mohan, Eero P. Simoncelli, Carlos Fernandez-Granda

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Kotlin∇: A Shape-Safe DSL for Differentiable Programming Breandan Considine, Michalis Famelis, Liam Paull

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Learned Imaging with Constraints and Uncertainty Quantification Felix J. Herrmann, Ali Siahkoohi, Gabrio Rizzuti

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Learning a Convolutional Bilinear Sparse Code for Natural Videos Dimitrios C. Gklezakos, Rajesh P. N. Rao

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Learning Network Parameters in the ReLU Model Arya Mazumdar, Ankit Singh Rawat

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Learning to Learn with Feedback and Local Plasticity Jack Lindsey

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Learning to Predict Visual Brain Activity by Predicting Future Sensory States Marcio Fonseca

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Learning to Recover Sparse Signals Sichen Zhong, Yue Zhao, Jianshu Chen

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Learning to Solve Linear Inverse Problems in Imaging with Neumann Networks Greg Ongie, Davis Gilton, Rebecca Willett

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Learning to Solve the Credit Assignment Problem Benjamin James Lansdell, Prashanth Prakash, Konrad Paul Kording

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Learning-Based Low-Rank Approximations Piotr Indyk, Ali Vakilian, Yang Yuan

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Local Unsupervised Learning for Image Analysis Leopold Grinberg, John Hopfield, Dmitry Krotov

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Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems Oscar Leong, Wesam Sakla

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Lower Bounds for Compressed Sensing with Generative Models Akshay Kamath, Sushrut Karmalkar, Eric Price

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Memory-Efficient Learning for Large-Scale Computational Imaging Michael Kellman, Jon Tamir, Emrah Bostan, Michael Lustig, Laura Waller

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Modelling Working Memory Using Deep Recurrent Reinforcement Learning Pravish Sainath, Pierre Bellec, Guillaume Lajoie

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Neocortical Plasticity: An Unsupervised Cake but No Free Lunch Eilif B. Muller, Philippe Beaudoin

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Neural Contract Element Extraction Revisited Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Ion Androutsopoulos

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Neural Reparameterization Improves Structural Optimization Stephan Hoyer, Jascha Sohl-Dickstein, Sam Greydanus

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On Domain Transfer When Predicting Intent in Text Petar Stojanov, Ahmed Hassan Awadallah, Paul Bennett, Saghar Hosseini

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On Recognition of Cyrillic Text Kostiantyn Liepieshov, Oles Dobosevych

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On the Adversarial Robustness of Neural Networks Without Weight Transport Mohamed Akrout

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PatchDIP Exploiting Patch Redundancy in Deep Image Prior for Denoising Muhammad Asim, Fahad Shamshad, Ali Ahmed

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Pattern Recognition of Labeled Concepts by a Single Spiking Neuron Model. Hannes Rapp, Martin Paul Nawrot, Merav Stern

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Phase Retrieval Using Untrained Neural Network Priors Gauri Jagatap, Chinmay Hegde

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Post-OCR Parsing: Building Simple and Robust Parser via BIO Tagging Wonseok Hwang, Seonghyeon Kim, Minjoon Seo, Jinyeong Yim, Seunghyun Park, Sungrae Park, Junyeop Lee, Bado Lee, Hwalsuk Lee

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Precise Asymptotics for Phase Retrieval and Compressed Sensing with Random Generative Priors Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborova

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Predictive Coding, Variational Autoencoders, and Biological Connections Joseph Marino

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PyMC4: Exploiting Coroutines for Implementing a Probabilistic Programming Framework Max Kochurov, Colin Carroll, Thomas Wiecki, Junpeng Lao

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Recurrent Neural Networks Learn Robust Representations by Dynamically Balancing Compression and Expansion Matthew Farrell, Stefano Recanatesi, Guillaume Lajoie, Eric Shea-Brown

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Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish

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Reinforcement Learning with a Network of Spiking Agents Sneha Aenugu, Abhishek Sharma, Sasikiran Yelamarthy, Hananel Hazan, Philip.S.Thomas, Robert Kozma

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Representation Learning in Geology and GilBERT Zikri Bayraktar, Hedi Driss, Marie Lefranc

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Repurposing Decoder-Transformer Language Models for Abstractive Summarization Luke de Oliveira, Alfredo Láinez Rodrigo

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Retrieving Signals with Deep Complex Extractors Chiheb Trabelsi, Olexa Bilaniuk, Ousmane Dia, Ying Zhang, Mirco Ravanelli, Jonathan Binas, Negar Rostamzadeh, Christopher J Pal

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Revealing Computational Mechanisms of Retinal Prediction via Model Reduction Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen A. Baccus, Surya Ganguli

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Revisit Recurrent Attention Model from an Active Sampling Perspective Jialin Lu

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Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rate and Global Landscape Analysis Shuang Qiu, Xiaohan Wei, Zhuoran Yang

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Sample Complexity Lower Bounds for Compressive Sensing with Generative Models Zhaoqiang Liu, Jonathan Scarlett

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Semantic Structure Extraction for Spreadsheet Tables with a Multi-Task Learning Architecture Haoyu Dong, Shijie Liu, Zhouyu Fu, Shi Han, Dongmei Zhang

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Significance of Feedforward Architectural Differences Between the Ventral Visual Stream and DenseNet Bryan Tripp

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Sparsity Programming: Automated Sparsity-Aware Optimizations in Differentiable Programming Shashi Gowda, Yingbo Ma, Valentin Churavy, Alan Edelman, Christopher Rackauckas

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Spike Sorting Using the Neural Clustering Process Yueqi Wang, Ari Pakman, Catalin Mitelut, JinHyung Lee, Liam Paninski

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Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory Robert Kim, Terrence J. Sejnowski

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Subsampled Fourier Ptychography via Pretrained Invertible and Untrained Network Priors Fahad Shamshad, Asif Hanif, Ali Ahmed

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SVDocNet: Spatially Variant U-Net for Blind Document Deblurring Bharat Mamidibathula, Prabir Kumar Biswas

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Taylor-Mode Automatic Differentiation for Higher-Order Derivatives in JAX Jesse Bettencourt, Matthew J. Johnson, David Duvenaud

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The Differentiable Curry Dimitrios Vytiniotis, Dan Belov, Richard Wei, Gordon Plotkin, Martin Abadi

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The Natural Tendency of Feed Forward Neural Networks to Favor Invariant Units Dean A. Pospisil, Wyeth Bair

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The Virtual Patch Clamp: Imputing C. Elegans Membrane Potentials from Calcium Imaging Andrew Warrington, Arthur Spencer, Frank Wood

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Towards Neural Similarity Evaluator Hassan Kané, Yusuf Kocyigit, Pelkins Ajanoh, Ali Abdalla, Mohamed Coulibali

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Towards Polyhedral Automatic Differentiation Jan Hückelheim, Navjot Kukreja

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Transforming Probabilistic Programs into Algebraic Circuits for Inference and Learning Pedro Zuidberg Dos Martires, Vincent Derkinderen, Robin Manhaeve, Wannes Meert, Angelika Kimmig, Luc De Raedt

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Transforming Recursive Programs for Parallel Execution Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman, Rif A. Saurous

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Translating Neural Signals to Text Using a Brain-Computer Interface Janaki Sheth, Ariel Tankus, Michelle Tran, Nader Pouratian, Itzhak Fried, William Speier

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Unrolled, Model-Based Networks for Lensless Imaging Kristina Monakhova, Joshua Yurtsever, Grace Kuo, Nick Antipa, Kyrollos Yanny, Laura Waller

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Unsupervised Deep Basis Pursuit: Learning Inverse Problems Without Ground-Truth Data Jonathan I. Tamir, Stella X. Yu, Michael Lustig

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Unsupervised Discovery of Dynamic Neural Circuits Colin Graber, Ryan Loh, Yurii Vlasov, Alexander Schwing

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What’s in a Functional Brain Parcellation? Gaël Varoquaux, Kamalakar Dadi, Arthur Mensch

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Working Memory Facilitates Reward-Modulated Hebbian Learning in Recurrent Neural Networks Roman Pogodin, Dane Corneil, Alexander Seeholzer, Joseph Heng, Wulfram Gerstner

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Y-Net: A Physics-Constrained and Semi-Supervised Learning Approach to the Phase Problem in Computational Electron Imaging Nouamane Laanait, Junqi Yin, Albina Borisevich

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