Harkirat Behl — accepted AI-conference papers

2026

Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning

Vaishnavi Shrivastava, Ahmed Hassan Awadallah, Vidhisha Balachandran, Shivam Garg, Harkirat Behl, Dimitris Papailiopoulos

ICLR 2026poster

Large language models trained with reinforcement learning on verifiable rewards often inflate response length—trading brevity for accuracy. While longer reasoning can help on hard problems, many extra tokens are filler: verbose text making little progress. We introduce GFPO (Group Filtered Policy Op…

Cited by 0 Source ↗code ↗Scholar ↗

2024

PEEKABOO: Interactive Video Generation via Masked-Diffusion

Yash Jain, Anshul Nasery, Vibhav Vineet, Harkirat Behl

CVPR 2024poster

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However a significant limitation is their inability to offer interactive control to users a feature that promises to open up unprecedented applications and creativity. In this work we introduc…

Cited by 39 Source ↗PDF ↗code ↗Scholar ↗

2023

DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets

Yash Jain, Harkirat Behl, Zsolt Kira, Vibhav Vineet

NeurIPS 2023poster

Construction of a universal detector poses a crucial question: How can we most effectively train a model on a large mixture of datasets? The answer lies in learning dataset-specific features and ensembling their knowledge but do all this in a single model. Previous methods achieve this by h…

Cited by 15 Source ↗PDF ↗code ↗Scholar ↗

2023

Efficiently Robustify Pre-Trained Models

Nishant Jain, Harkirat Behl, Yogesh Singh Rawat, Vibhav Vineet

ICCV 2023poster

A recent trend in deep learning algorithms has been towards training large scale models, having high parameter count and trained on big dataset. However, robustness of such large scale models towards real-world settings is still a less-explored topic. In this work, we first benchmark the performance…

Cited by 5 PDF ↗Scholar ↗

2023

Generalized Decoding for Pixel, Image, and Language

Xueyan Zou, Zi-Yi Dou, Jianwei Yang, Zhe Gan, Linjie Li, Chunyuan Li +8

CVPR 2023poster

We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decoder takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-l…

Cited by 273 Source ↗PDF ↗code ↗Scholar ↗

2022

Neural-Sim: Learning to Generate Training Data with NeRF

Yunhao Ge, Harkirat Behl, Jiashu Xu, Suriya Gunasekar, Neel Joshi, Yale Song +3

ECCV 2022poster

"Traditional approaches for training a computer vision models requires collecting and labelling vast amounts of imagery under a diverse set of scene configurations and properties. This process is incredibly time-consuming, and it is challenging to ensure that the captured data distribution maps well…

Cited by 35 Source ↗PDF ↗code ↗Scholar ↗

2021

Progressive Skeletonization: Trimming more fat from a network at initialization

Pau de Jorge, Amartya Sanyal, Harkirat Behl, Philip Torr, Grégory Rogez, Puneet K. Dokania

ICLR 2021poster

Recent studies have shown that skeletonization (pruning parameters) of networks at initialization provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance. However, we observe that beyond a certain level of sparsity (appro…

Cited by 118 Source ↗PDF ↗code ↗Scholar ↗

2021

Scaling the Convex Barrier with Active Sets

Alessandro De Palma, Harkirat Behl, Rudy R Bunel, Philip Torr, M. Pawan Kumar

ICLR 2021poster

Tight and efficient neural network bounding is of critical importance for the scaling of neural network verification systems. A number of efficient specialised dual solvers for neural network bounds have been presented recently, but they are often too loose to verify more challenging properties. Thi…

Cited by 71 Source ↗PDF ↗Scholar ↗

2020

STEER : Simple Temporal Regularization For Neural ODE

Arnab Ghosh, Harkirat Behl, Emilien Dupont, Philip Torr, Vinay Namboodiri

NeurIPS 2020poster

Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive. Indeed, computing the forward pass of such models involves solving an ODE which can become arbitrarily complex during training. Recent works have shown that regularizing the dynamics of the ODE can partially a…

Cited by 99 Source ↗PDF ↗code ↗Scholar ↗