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Manish Kumar Singh

5 accepted papers

2025

PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer

Pierre-David Letourneau, Manish Kumar Singh, Hsin-Pai Cheng, Shizhong Han, Yunxiao Shi, Dalton Jones +3
ICLR 2025poster

We present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models. Notably, several recent alternative attention mechanisms, including Hyena, Mamba, SimA, Conv2Former, and Castling-ViT, can…

Cited by 2Source ↗PDF ↗Scholar ↗
2024

DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions

Yunxiao Shi, Manish Kumar Singh, Hong Cai, Fatih Porikli
CVPR 2024poster

In this paper we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. Specifically we first enhance a baseline convolutional depth completion model by applying attention to 2D features in the…

Cited by 3Source ↗PDF ↗Scholar ↗
2024

FutureDepth: Learning to Predict the Future Improves Video Depth Estimation

Rajeev Yasarla*, Manish Kumar Singh, Hong Cai, Yunxiao Shi, Jisoo Jeong, Yinhao Zhu +3
ECCV 2024poster

"In this paper, we propose a novel video depth estimation approach, , which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training. More specifically, we propose a future prediction network, F-Net, which t…

Cited by 5Source ↗PDF ↗Scholar ↗
2023

Physics-Informed Transfer Learning for Voltage Stability Margin Prediction

Manish Kumar Singh, Konstantinos D. Polyzos, Panagiotis A. Traganitis, Sairaj V. Dhople, Georgios B. Giannakis
ICASSP 2023accepted

Assessing set-membership and evaluating distances to the related set boundary are problems of widespread interest, and can often be computationally challenging. Seeking efficient learning models for such tasks, this paper deals with voltage stability margin prediction for power systems. Supervised t…

Cited by 0Source ↗Scholar ↗
2021

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel Riek, Kamalika Chaudhuri
AISTATS 2021poster

In many real-world applications, multiple agents seek to learn how to perform highly related yet slightly different tasks in an online bandit learning protocol. We formulate this problem as the $\epsilon$-multi-player multi-armed bandit problem, in which a set of players concurrently interact with a…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗