ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
title stringlengths 15 138 | paper_url stringlengths 42 42 | authors listlengths 1 35 | type stringclasses 3
values | primary_area stringclasses 20
values | abstract large_stringlengths 480 3.09k | keywords listlengths 1 27 | TL;DR large_stringlengths 21 250 ⌀ | submission_number int64 5 9.5k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values | embedding listlengths 768 768 |
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TabR: Tabular Deep Learning Meets Nearest Neighbors | https://openreview.net/forum?id=rhgIgTSSxW | [
"Yury Gorishniy",
"Ivan Rubachev",
"Nikolay Kartashev",
"Daniil Shlenskii",
"Akim Kotelnikov",
"Artem Babenko"
] | Poster | general machine learning (i.e., none of the above) | Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers.
However, despite the recent efforts, the non-DL algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution for these problems.
One of... | [
"tabular",
"tabular data",
"architecture",
"deep learning",
"neural networks"
] | TabR is a new tabular DL model with a k-nearest-neighbors-like component and strong results on public benchmarks. | 9,502 | 2307.14338 | title_judge | [
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SaNN: Simple Yet Powerful Simplicial-aware Neural Networks | https://openreview.net/forum?id=eUgS9Ig8JG | [
"Sravanthi Gurugubelli",
"Sundeep Prabhakar Chepuri"
] | Spotlight | learning on graphs and other geometries & topologies | Simplicial neural networks (SNNs) are deep models for higher-order graph representation learning. SNNs learn low-dimensional embeddings of simplices in a simplicial complex by aggregating features of their respective upper, lower, boundary, and coboundary adjacent simplices. The aggregation in SNNs is carried out durin... | [
"Graph Neural Networks",
"Higher-order Representation Learning",
"Simplicial Complexes",
"Simplicial Neural Networks",
"Weisfeiler-Lehman Isomorphism Test"
] | null | 9,491 | null | null | [
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Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models | https://openreview.net/forum?id=qBL04XXex6 | [
"Sijia Chen",
"Baochun Li",
"Di Niu"
] | Poster | generative models | The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thought demonstrations as exemplars in prompts. Recent work, e.g., Tree of Thoughts, has pointed out the importance of exploration and self-evalu... | [
"Large Language Models; Prompt Engineering; Boosting Mechanism;"
] | null | 9,482 | 2402.11140 | title_snapshot | [
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Beyond Memorization: Violating Privacy via Inference with Large Language Models | https://openreview.net/forum?id=kmn0BhQk7p | [
"Robin Staab",
"Mark Vero",
"Mislav Balunovic",
"Martin Vechev"
] | Spotlight | societal considerations including fairness, safety, privacy | Current privacy research on large language models (LLMs) primarily focuses on the issue of extracting memorized training data. At the same time, models’ inference capabilities have increased drastically. This raises the key question of whether current LLMs could violate individuals’ privacy by inferring personal attrib... | [
"Privacy",
"Large Language Models"
] | We present the first comprehensive study on the capabilities of pretrained LLMs to infer personal attributes from texts given at inference. | 9,451 | 2310.07298 | title_snapshot | [
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Locality Sensitive Sparse Encoding for Learning World Models Online | https://openreview.net/forum?id=i8PjQT3Uig | [
"Zichen Liu",
"Chao Du",
"Wee Sun Lee",
"Min Lin"
] | Poster | reinforcement learning | Acquiring an accurate world model $\textit{online}$ for model-based reinforcement learning (MBRL) is challenging due to data nonstationarity, which typically causes catastrophic forgetting for neural networks (NNs). From the online learning perspective, a Follow-The-Leader (FTL) world model is desirable, which optimall... | [
"model-based rl",
"online learning",
"incremental learning",
"catastrophic forgetting"
] | null | 9,441 | 2401.13034 | title_snapshot | [
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Enhancing Neural Subset Selection: Integrating Background Information into Set Representations | https://openreview.net/forum?id=eepoE7iLpL | [
"Binghui Xie",
"Yatao Bian",
"Kaiwen Zhou",
"Yongqiang Chen",
"Peilin Zhao",
"Bo Han",
"Wei Meng",
"James Cheng"
] | Poster | representation learning for computer vision, audio, language, and other modalities | Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodologies in the field primarily concentrate on constructing models that capture the relationship between utility function values and subsets with... | [
"Neural Set Function",
"Hierarchical Structure",
"Invariance",
"Subset Selection"
] | null | 9,406 | 2402.03139 | title_snapshot | [
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Bridging Vision and Language Spaces with Assignment Prediction | https://openreview.net/forum?id=lK2V2E2MNv | [
"Jungin Park",
"Jiyoung Lee",
"Kwanghoon Sohn"
] | Poster | representation learning for computer vision, audio, language, and other modalities | This paper introduces VLAP, a novel approach that bridges pretrained vision models and large language models (LLMs) to make frozen LLMs understand the visual world. VLAP transforms the embedding space of pretrained vision models into the LLMs' word embedding space using a single linear layer for efficient and general-p... | [
"Multimodal learning",
"vision-language tasks",
"frozen LLMs",
"optimal transport",
"assignment prediction"
] | This paper presents to bridge frozen image encoders and large language models (LLMs) for grounding LLMs to images. | 9,396 | 2404.09632 | title_snapshot | [
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Generative Judge for Evaluating Alignment | https://openreview.net/forum?id=gtkFw6sZGS | [
"Junlong Li",
"Shichao Sun",
"Weizhe Yuan",
"Run-Ze Fan",
"hai zhao",
"Pengfei Liu"
] | Poster | generative models | The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address. In the field of Natural Language Processing (NLP), researchers have shifted their focus from conventional NLP tasks (e.g., sequence tagging and parsing) towards tasks that revolve around aligning with h... | [
"Generative",
"Evaluation",
"Alignment"
] | We release Auto-J, a cutting-edge, flexible and interpretable judge with 13B parameters, to evaluate alignment in various real-world scenarios. | 9,392 | 2310.05470 | title_snapshot | [
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Rethinking and Extending the Probabilistic Inference Capacity of GNNs | https://openreview.net/forum?id=7vVWiCrFnd | [
"Tuo Xu",
"Lei Zou"
] | Poster | learning on graphs and other geometries & topologies | Designing expressive Graph Neural Networks (GNNs) is an important topic in graph machine learning fields. Despite the existence of numerous approaches proposed to enhance GNNs based on Weisfeiler-Lehman (WL) tests, what GNNs can and cannot learn still lacks a deeper understanding. This paper adopts a fundamentally diff... | [
"graph neural networks",
"expressiveness",
"approximate inference"
] | Discuss and extend GNNs' expressive power for probabilistic inference. | 9,389 | null | null | [
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Learning model uncertainty as variance-minimizing instance weights | https://openreview.net/forum?id=bDWXhzZT40 | [
"Nishant Jain",
"Karthikeyan Shanmugam",
"Pradeep Shenoy"
] | Poster | general machine learning (i.e., none of the above) | Predictive uncertainty--a model’s self-awareness regarding its accuracy on an input--is key for both building robust models via training interventions and for test-time applications such as selective classification. We propose a novel instance-conditional reweighting approach that captures predictive uncertainty using ... | [
"loss reweighting",
"epistemic uncertainty",
"bi-level optimization",
"model calibration",
"bayesian neural networks"
] | null | 9,383 | null | null | [
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