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20232026
most citedStructure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks

2 citations · 5 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.LG2025

Sparse classification with positive-confidence data in high dimensions

The Tien Mai, Mai Anh Nguyen, Trung Nghia Nguyen

High-dimensional learning problems, where the number of features exceeds the sample size, often require sparse regularization for effective prediction and variable selection. While…

cs.LG2025

Near-Polynomially Competitive Active Logistic Regression

Yihan Zhou, Eric Price, Trung Nguyen

We address the problem of active logistic regression in the realizable setting. It is well known that active learning can require exponentially fewer label queries compared to pass…

cs.LG2024★ 1 cited

Accelerating Transformers with Spectrum-Preserving Token Merging

Hoai-Chau Tran, Duy M. H. Nguyen, Duy M. Nguyen +7

Increasing the throughput of the Transformer architecture, a foundational component used in numerous state-of-the-art models for vision and language tasks (e.g., GPT, LLaVa), is an…

cs.LG2024★ 1 cited

CompeteSMoE -- Effective Training of Sparse Mixture of Experts via Competition

Quang Pham, Giang Do, Huy Nguyen +8

Sparse mixture of experts (SMoE) offers an appealing solution to scale up the model complexity beyond the mean of increasing the network's depth or width. However, effective traini…

cs.LG2024★ 2 cited

Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks

Duy M. H. Nguyen, Nina Lukashina, Tai Nguyen +7

A molecule's 2D representation consists of its atoms, their attributes, and the molecule's covalent bonds. A 3D (geometric) representation of a molecule is called a conformer and c…

cs.LG2023

HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts

Giang Do, Khiem Le, Quang Pham +7

By routing input tokens to only a few split experts, Sparse Mixture-of-Experts has enabled efficient training of large language models. Recent findings suggest that fixing the rout…