ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
title stringlengths 18 162 | paper_url stringlengths 42 44 | authors listlengths 1 29 | type stringclasses 3
values | primary_area stringclasses 13
values | abstract large_stringlengths 400 2.37k | keywords listlengths 0 22 | TL;DR large_stringclasses 0
values | submission_number int64 9 6.62k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | embedding listlengths 768 768 |
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Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics | https://openreview.net/forum?id=RUzSobdYy0V | [
"Julius Adebayo",
"Melissa Hall",
"Bowen Yu",
"Bobbie Chern"
] | Poster | Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics) | Errors in labels obtained via human annotation adversely affect a trained model's performance. Existing approaches propose ways to mitigate the effect of label error on a model's downstream accuracy, yet little is known about its impact on a model's group-based disparity metrics\footnote{Group-based disparity metrics l... | [] | null | 6,620 | 2310.02533 | title_snapshot | [
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Factorized Fourier Neural Operators | https://openreview.net/forum?id=tmIiMPl4IPa | [
"Alasdair Tran",
"Alexander Mathews",
"Lexing Xie",
"Cheng Soon Ong"
] | Poster | Machine Learning for Sciences (eg biology, physics, health sciences, social sciences, climate/sustainability ) | We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial differential equations (PDEs). Starting from a recently proposed Fourier representation of flow fields, the F-FNO bridges the performance gap between pure machine learning approaches to that of the best numerical... | [
"fourier transform",
"fourier operators",
"pde",
"navier stokes"
] | null | 6,610 | 2111.13802 | title_snapshot | [
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DFPC: Data flow driven pruning of coupled channels without data. | https://openreview.net/forum?id=mhnHqRqcjYU | [
"Tanay Narshana",
"Chaitanya Murti",
"Chiranjib Bhattacharyya"
] | Poster | Deep Learning and representational learning | Modern, multi-branched neural network architectures often possess complex interconnections between layers, which we call coupled channels (CCs). Structured pruning of CCs in these multi-branch networks is an under-researched problem, as most existing works are typically designed for pruning single-branch models like VG... | [
"Pruning",
"Data Free",
"Model Compression"
] | null | 6,603 | null | null | [
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TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning | https://openreview.net/forum?id=sZI1Oj9KBKy | [
"Chaitanya Murti",
"Tanay Narshana",
"Chiranjib Bhattacharyya"
] | Poster | Deep Learning and representational learning | Achieving structured, data-free sparsity of deep neural networks (DNNs) remains an open area of research. In this work, we address the challenge of pruning filters without access to the original training set or loss function. We propose the discriminative filters hypothesis, that well-trained models possess discrimina... | [
"Structured pruning",
"model compression"
] | null | 6,601 | null | null | [
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Finding Actual Descent Directions for Adversarial Training | https://openreview.net/forum?id=I3HCE7Ro78H | [
"Fabian Latorre",
"Igor Krawczuk",
"Leello Tadesse Dadi",
"Thomas Pethick",
"Volkan Cevher"
] | Poster | Optimization (eg, convex and non-convex optimization) | Adversarial Training using a strong first-order adversary (PGD) is the gold standard for training Deep Neural Networks that are robust to adversarial examples. We show that, contrary to the general understanding of the method, the gradient at an optimal adversarial example may increase, rather than decrease, the advers... | [
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"non-convex optimization",
"robustness"
] | null | 6,599 | null | null | [
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Learning Continuous Normalizing Flows For Faster Convergence To Target Distribution via Ascent Regularizations | https://openreview.net/forum?id=6iEoTr-jeB7 | [
"Shuangshuang Chen",
"Sihao Ding",
"Yiannis Karayiannidis",
"Mårten Björkman"
] | Poster | Probabilistic Methods (eg, variational inference, causal inference, Gaussian processes) | Normalizing flows (NFs) have been shown to be advantageous in modeling complex distributions and improving sampling efficiency for unbiased sampling. In this work, we propose a new class of continuous NFs, ascent continuous normalizing flows (ACNFs), that makes a base distribution converge faster to a target distribut... | [
"normalizing flows",
"gradient flows",
"density estimation",
"unbiased sampling",
"variational inference"
] | null | 6,593 | null | null | [
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Softened Symbol Grounding for Neuro-symbolic Systems | https://openreview.net/forum?id=HTJE5Krui0g | [
"Zenan Li",
"Yuan Yao",
"Taolue Chen",
"Jingwei Xu",
"Chun Cao",
"Xiaoxing Ma",
"Jian L\\\"{u}"
] | Poster | Deep Learning and representational learning | Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving,
whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol grounding process, bridging the gap between the two worlds, and resultin... | [
"neuro-symbolic learning",
"symbol grounding problem",
"projection-based sampling"
] | null | 6,551 | 2403.00323 | title_snapshot | [
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Encoding Recurrence into Transformers | https://openreview.net/forum?id=7YfHla7IxBJ | [
"Feiqing Huang",
"Kexin Lu",
"Yuxi CAI",
"Zhen Qin",
"Yanwen Fang",
"Guangjian Tian",
"Guodong Li"
] | Notable-top-5% | Deep Learning and representational learning | This paper novelly breaks down with ignorable loss an RNN layer into a sequence of simple RNNs, each of which can be further rewritten into a lightweight positional encoding matrix of a self-attention, named the Recurrence Encoding Matrix (REM). Thus, recurrent dynamics introduced by the RNN layer can be encapsulated i... | [
"Recurrent models",
"Transformers",
"sample efficiency",
"gated mechanism"
] | null | 6,550 | null | null | [
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Human-Guided Fair Classification for Natural Language Processing | https://openreview.net/forum?id=N_g8TT9Cy7f | [
"Florian E. Dorner",
"Momchil Peychev",
"Nikola Konstantinov",
"Naman Goel",
"Elliott Ash",
"Martin Vechev"
] | Notable-top-25% | Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics) | Text classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as gender or ethnicity. However, there is a gap between human intuition abo... | [
"Individual Fairness",
"Style Transfer",
"NLP",
"Crowdsourcing",
"Human Evaluation"
] | null | 6,549 | 2212.10154 | title_snapshot | [
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Mini-batch $k$-means terminates within $O(d/\epsilon)$ iterations | https://openreview.net/forum?id=jREF4bkfi_S | [
"Gregory Schwartzman"
] | Poster | Theory (eg, control theory, learning theory, algorithmic game theory) | We answer the question: "Does \emph{local} progress (on batches) imply \emph{global} progress (on the entire dataset) for mini-batch $k$-means?". Specifically, we consider mini-batch $k$-means which terminates only when the improvement in the quality of the clustering on the sampled batch is below some threshold.
Alth... | [] | null | 6,542 | 2304.00419 | title_snapshot | [
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0... |
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