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67.09/100

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全站排名 #2,028

发表论文15 篇

平均评分5.3

年均产出5.0 篇/年

Vikas Singh

Professor@University of Wisconsin, Madison·美国·OpenReview

研究方向

brain imaging · continuous and discrete optimization in vision and machine learning · statistical image analysis · deep learning

4.7

CafeQ: Calibration-free Quantization via Learned Transformations and Adaptive Rounding

ICLR 2026Withdrawn

3.0

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models

ICLR 2026Withdrawn

通讯

7.3

Single-Step Operator Learning for Conditioned Time-Series Diffusion Models

NeurIPS 2025Poster

二作

7.3

Composing Linear Layers from Irreducibles

NeurIPS 2025Poster

三作

7.3

FoGE: Fock Space inspired encoding for graph prompting

NeurIPS 2025Poster

三作

6.8

PINNs with Learnable Quadrature

NeurIPS 2025Poster

三作

6.8

SimpleTM: A Simple Baseline for Multivariate Time Series Forecasting

ICLR 2025Poster

通讯

6.4

Concept Attractors in LLMs and their Applications

NeurIPS 2025Rejected

二作

5.3

FoGE: Fock Space inspired encoding for graph prompting

ICLR 2025Rejected

三作

4.5

Solving PDEs via learnable quadrature

ICLR 2025Rejected

三作

合作者 (20)

Sotirios Panagiotis Chytas

Kamyar Azizzadenesheli

2 篇

Vishnu Suresh Lokhande

1 篇

Vikas Singh

MnemoDyn: Learning Resting State Dynamics from $40$K FMRI sequences

Concept Attractors in LLMs and their Applications

Fine-Tuning of Transformer models with Frames

CafeQ: Calibration-free Quantization via Learned Transformations and Adaptive Rounding

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models

Single-Step Operator Learning for Conditioned Time-Series Diffusion Models

Composing Linear Layers from Irreducibles

FoGE: Fock Space inspired encoding for graph prompting

PINNs with Learnable Quadrature

SimpleTM: A Simple Baseline for Multivariate Time Series Forecasting

Concept Attractors in LLMs and their Applications

FoGE: Fock Space inspired encoding for graph prompting

Solving PDEs via learnable quadrature