ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
title stringlengths 12 151 | paper_url stringlengths 41 43 | authors listlengths 1 40 | type stringclasses 3
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values | abstract large_stringlengths 519 2.34k | keywords listlengths 0 19 | TL;DR large_stringclasses 0
values | submission_number int64 1 4.72k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values | embedding listlengths 768 768 |
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Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream | https://openreview.net/forum?id=g1SzIRLQXMM | [
"Franziska Geiger",
"Martin Schrimpf",
"Tiago Marques",
"James J. DiCarlo"
] | Spotlight | null | After training on large datasets, certain deep neural networks are surprisingly good models of the neural mechanisms of adult primate visual object recognition. Nevertheless, these models are considered poor models of the development of the visual system because they posit millions of sequential, precisely coordinated ... | [
"computational neuroscience",
"primate visual ventral stream",
"convolutional neural networks",
"biologically plausible learning"
] | null | 4,724 | null | null | [
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Learning to Downsample for Segmentation of Ultra-High Resolution Images | https://openreview.net/forum?id=HndgQudNb91 | [
"Chen Jin",
"Ryutaro Tanno",
"Thomy Mertzanidou",
"Eleftheria Panagiotaki",
"Daniel C. Alexander"
] | Poster | null | Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. Common solutions uniformly downsample the input images to meet memory constraints, assuming all pixels are equally informative. In this work,... | [
"ultra-high resolution image segmentation",
"non-uniform dowmsampling",
"efficient segmentation",
"large volume image segmentation",
"medical image segmentation"
] | null | 4,722 | 2109.11071 | title_snapshot | [
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Variational Neural Cellular Automata | https://openreview.net/forum?id=7fFO4cMBx_9 | [
"Rasmus Berg Palm",
"Miguel González Duque",
"Shyam Sudhakaran",
"Sebastian Risi"
] | Poster | null | In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms --- algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process.
Inspired by the incredible diversity of this biological generative process, we propose a generative model... | [
"Neural Cellular Automata",
"Cellular Automata",
"Self-Organization",
"Generative Models"
] | null | 4,721 | 2201.12360 | title_snapshot | [
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Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation | https://openreview.net/forum?id=FKp8-pIRo3y | [
"Todor Davchev",
"Oleg Olegovich Sushkov",
"Jean-Baptiste Regli",
"Stefan Schaal",
"Yusuf Aytar",
"Markus Wulfmeier",
"Jon Scholz"
] | Poster | null | Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of ``narrow passages'' in their state space. Solving such tasks with a sparse reward in a sample-efficient manner poses a challenge to modern reinforcement learning (RL) due to the associated long-horizon nature ... | [
"goal-conditioned reinforcement learning",
"learning from demonstrations",
"long-horizon dexterous manipulation",
"bi-manual manipulation"
] | null | 4,719 | 2112.00597 | title_snapshot | [
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L0-Sparse Canonical Correlation Analysis | https://openreview.net/forum?id=KntaNRo6R48 | [
"Ofir Lindenbaum",
"Moshe Salhov",
"Amir Averbuch",
"Yuval Kluger"
] | Poster | null | Canonical Correlation Analysis (CCA) models are powerful for studying the associations between two sets of variables. The canonically correlated representations, termed \textit{canonical variates} are widely used in unsupervised learning to analyze unlabeled multi-modal registered datasets. Despite their success, CCA m... | [] | null | 4,717 | null | null | [
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Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? | https://openreview.net/forum?id=B7ZbqNLDn-_ | [
"Sheikh Shams Azam",
"Seyyedali Hosseinalipour",
"Qiang Qiu",
"Christopher Brinton"
] | Poster | null | In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank characteristics of the subspace spanned by gradients (i.e., the gradient-space) in centralized model training, and observe that the gradient-spac... | [
"Distributed Machine Learning",
"Federated Learning",
"Gradient Subspace",
"SGD"
] | null | 4,715 | 2202.00280 | title_snapshot | [
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Is Homophily a Necessity for Graph Neural Networks? | https://openreview.net/forum?id=ucASPPD9GKN | [
"Yao Ma",
"Xiaorui Liu",
"Neil Shah",
"Jiliang Tang"
] | Poster | null | Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node classification, GNNs are widely believed to work well due to the homophily assumption (``like attracts like''), and fail to generalize to hete... | [] | null | 4,711 | 2106.06134 | title_snapshot | [
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DEGREE: Decomposition Based Explanation for Graph Neural Networks | https://openreview.net/forum?id=Ve0Wth3ptT_ | [
"Qizhang Feng",
"Ninghao Liu",
"Fan Yang",
"Ruixiang Tang",
"Mengnan Du",
"Xia Hu"
] | Poster | null | Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their applicability. Whereas explaining GNNs remains a challenge, most existing methods fall into approximatio... | [
"XAI",
"GNN"
] | null | 4,703 | 2305.12895 | title_snapshot | [
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Improving Mutual Information Estimation with Annealed and Energy-Based Bounds | https://openreview.net/forum?id=T0B9AoM_bFg | [
"Rob Brekelmans",
"Sicong Huang",
"Marzyeh Ghassemi",
"Greg Ver Steeg",
"Roger Baker Grosse",
"Alireza Makhzani"
] | Poster | null | Mutual information (MI) is a fundamental quantity in information theory and machine learning. However, direct estimation of MI is intractable, even if the true joint probability density for the variables of interest is known, as it involves estimating a potentially high-dimensional log partition function. In this work,... | [
"mutual information estimation",
"annealed importance sampling",
"energy-based models"
] | null | 4,668 | 2303.06992 | title_snapshot | [
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Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods | https://openreview.net/forum?id=bp-LJ4y_XC | [
"Xueyuan She",
"Saurabh Dash",
"Saibal Mukhopadhyay"
] | Poster | null | A dynamical system of spiking neurons with only feedforward connections can classify spatiotemporal patterns without recurrent connections. However, the theoretical construct of a feedforward spiking neural network (SNN) for approximating a temporal sequence remains unclear, making it challenging to optimize SNN archit... | [
"spiking neural network",
"spatiotemporal processing",
"feedforward network"
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