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20232026
most citedTowards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives

6 citations · 16 across the 15 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.LG2024

MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification

Siddhant Bikram Shah, Shuvam Shiwakoti, Maheep Chaudhary +1

The complexity of text-embedded images presents a formidable challenge in machine learning given the need for multimodal understanding of multiple aspects of expression conveyed by…

cs.AI2024

Simple Unsupervised Knowledge Distillation With Space Similarity

Aditya Singh, Haohan Wang

As per recent studies, Self-supervised learning (SSL) does not readily extend to smaller architectures. One direction to mitigate this shortcoming while simultaneously training a s…

cs.LG2024★ 4 cited

GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis

Haoyang Liu, Shuyu Chen, Ye Zhang +1

Recent advancements in machine learning have significantly improved the identification of disease-associated genes from gene expression datasets. However, these processes often req…

cs.CV2024

Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation

Yijiang Li, Sucheng Ren, Weipeng Deng +4

Out-of-distribution (OOD) generalization is a favorable yet challenging property for deep neural networks. The core challenges lie in the limited availability of source domains tha…

cs.CV2024

Approximate Nullspace Augmented Finetuning for Robust Vision Transformers

Haoyang Liu, Aditya Singh, Yijiang Li +1

Enhancing the robustness of deep learning models, particularly in the realm of vision transformers (ViTs), is crucial for their real-world deployment. In this work, we provide a fi…

cs.LG2024

Towards Adversarially Robust Dataset Distillation by Curvature Regularization

Eric Xue, Yijiang Li, Haoyang Liu +3

Dataset distillation (DD) allows datasets to be distilled to fractions of their original size while preserving the rich distributional information, so that models trained on the di…

q-bio.GN2024★ 4 cited

Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data

Haoyang Liu, Yijiang Li, Jinglin Jian +7

Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets. For instance, it has facilitate…