ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
title stringlengths 19 143 | paper_url stringlengths 41 43 | authors listlengths 1 23 | type stringclasses 3
values | primary_area stringclasses 0
values | abstract large_stringlengths 457 2.38k | keywords listlengths 0 21 | TL;DR large_stringclasses 0
values | submission_number int64 1 3.83k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values | embedding listlengths 768 768 |
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Net-DNF: Effective Deep Modeling of Tabular Data | https://openreview.net/forum?id=73WTGs96kho | [
"Liran Katzir",
"Gal Elidan",
"Ran El-Yaniv"
] | Poster | null | A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is still no widely accepted neural architecture that dominates tabular data. As a step toward bridging this gap, we present Net-DNF a novel ge... | [
"Neural Networks",
"Architectures",
"Tabular Data",
"Predictive Modeling"
] | null | 1,574 | null | null | [
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Predicting Inductive Biases of Pre-Trained Models | https://openreview.net/forum?id=mNtmhaDkAr | [
"Charles Lovering",
"Rohan Jha",
"Tal Linzen",
"Ellie Pavlick"
] | Poster | null | Most current NLP systems are based on a pre-train-then-fine-tune paradigm, in which a large neural network is first trained in a self-supervised way designed to encourage the network to extract broadly-useful linguistic features, and then fine-tuned for a specific task of interest. Recent work attempts to understand wh... | [
"information-theoretical probing",
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"challenge sets",
"natural language processing"
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Optimism in Reinforcement Learning with Generalized Linear Function Approximation | https://openreview.net/forum?id=CBmJwzneppz | [
"Yining Wang",
"Ruosong Wang",
"Simon Shaolei Du",
"Akshay Krishnamurthy"
] | Poster | null | We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call ``optimistic closure,'' which is strictly weaker than assumptions from prior analyses for the linear setting. With op... | [
"reinforcement learning",
"optimism",
"exploration",
"function approximation",
"theory",
"regret analysis",
"provable sample efficiency"
] | null | 3,820 | 1912.04136 | title_snapshot | [
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SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing | https://openreview.net/forum?id=oyZxhRI2RiE | [
"Tao Yu",
"Rui Zhang",
"Alex Polozov",
"Christopher Meek",
"Ahmed Hassan Awadallah"
] | Poster | null | Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e.g., SQL, SPARQL) that can be executed against a structured ontology (e.g. databases, knowledge bases). To accomplish this task, a CSP system needs to model the relation between the ... | [] | null | 3,773 | null | null | [
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A teacher-student framework to distill future trajectories | https://openreview.net/forum?id=ECuvULjFQia | [
"Alexander Neitz",
"Giambattista Parascandolo",
"Bernhard Schölkopf"
] | Poster | null | By learning to predict trajectories of dynamical systems, model-based methods can make extensive use of all observations from past experience. However, due to partial observability, stochasticity, compounding errors, and irrelevant dynamics, training to predict observations explicitly often results in poor models. Mode... | [
"meta-learning",
"privileged information"
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Certify or Predict: Boosting Certified Robustness with Compositional Architectures | https://openreview.net/forum?id=USCNapootw | [
"Mark Niklas Mueller",
"Mislav Balunovic",
"Martin Vechev"
] | Poster | null | A core challenge with existing certified defense mechanisms is that while they improve certified robustness, they also tend to drastically decrease natural accuracy, making it difficult to use these methods in practice. In this work, we propose a new architecture which addresses this challenge and enables one to boost ... | [
"Provable Robustness",
"Network Architecture",
"Robustness",
"Adversarial Accuracy",
"Certified Robustness"
] | null | 3,751 | null | null | [
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On the Transfer of Disentangled Representations in Realistic Settings | https://openreview.net/forum?id=8VXvj1QNRl1 | [
"Andrea Dittadi",
"Frederik Träuble",
"Francesco Locatello",
"Manuel Wuthrich",
"Vaibhav Agrawal",
"Ole Winther",
"Stefan Bauer",
"Bernhard Schölkopf"
] | Poster | null | Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and r... | [
"representation learning",
"disentanglement",
"real-world"
] | null | 3,746 | 2010.14407 | title_snapshot | [
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Robust Reinforcement Learning on State Observations with Learned Optimal Adversary | https://openreview.net/forum?id=sCZbhBvqQaU | [
"Huan Zhang",
"Hongge Chen",
"Duane S Boning",
"Cho-Jui Hsieh"
] | Poster | null | We study the robustness of reinforcement learning (RL) with adversarially perturbed state observations, which aligns with the setting of many adversarial attacks to deep reinforcement learning (DRL) and is also important for rolling out real-world RL agent under unpredictable sensing noise. With a fixed agent policy, w... | [
"reinforcement learning",
"robustness",
"adversarial attacks",
"adversarial defense"
] | null | 3,741 | 2101.08452 | title_snapshot | [
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Practical Real Time Recurrent Learning with a Sparse Approximation | https://openreview.net/forum?id=q3KSThy2GwB | [
"Jacob Menick",
"Erich Elsen",
"Utku Evci",
"Simon Osindero",
"Karen Simonyan",
"Alex Graves"
] | Spotlight | null | Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network states, and prohibits updating the weights "online" (after every timestep). Real Time Recurrent Learning (RTRL) eliminates the need for history storage and allows for online weight updat... | [
"recurrent neural networks",
"backpropagation",
"biologically plausible",
"forward mode",
"real time recurrent learning",
"rtrl",
"bptt"
] | null | 3,731 | 2006.07232 | title_judge | [
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Retrieval-Augmented Generation for Code Summarization via Hybrid GNN | https://openreview.net/forum?id=zv-typ1gPxA | [
"Shangqing Liu",
"Yu Chen",
"Xiaofei Xie",
"Jing Kai Siow",
"Yang Liu"
] | Spotlight | null | Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Mo... | [
"Code Summarization",
"Graph Neural Network",
"Retrieval",
"Generation"
] | null | 3,719 | 2006.05405 | title_snapshot | [
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Learning from others' mistakes: Avoiding dataset biases without modeling them | https://openreview.net/forum?id=Hf3qXoiNkR | [
"Victor Sanh",
"Thomas Wolf",
"Yonatan Belinkov",
"Alexander M Rush"
] | Poster | null | State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We consider cases... | [
"dataset bias",
"product of experts",
"natural language processing"
] | null | 3,718 | 2012.01300 | title_snapshot | [
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