1 citations · 1 across the 14 of their papers we have counts for
17 papers
Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding
Raghavv Goel, Sudhanshu Agrawal, Mukul Gagrani +2
Decoder hidden states from different layers of large language models (LLMs) often encode complementary information. However, existing speculative decoding methods typically rely on…
Score-Calibrated Flow for Sampling from Unnormalized Densities with Applications to Generative Online Reinforcement Learning
Zeyang Li, Yunan Wang, Risheek Garrepalli +2
Diffusion and flow models provide expressive policy classes for online reinforcement learning (RL), enabling multimodal behaviors and improved performance. However, training these…
Streaming Interventions: Can Video Large Language Models Correct Mistakes as They Occur?
Apratim Bhattacharyya, Shweta Mahajan, Sanjay Haresh +5
Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video (and multimodal) large lang…
Generative Scenario Rollouts for End-to-End Autonomous Driving
Rajeev Yasarla, Deepti Hegde, Shizhong Han +10
Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly rely on imitation lear…
Masks Can Be Distracting: On Context Comprehension in Diffusion Language Models
Julianna Piskorz, Cristina Pinneri, Alvaro Correia +3
Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to Autoregressive Language Models (ARLMs), leveraging a denoising objective that, in princ…
MultiHuman-Testbench: Benchmarking Image Generation for Multiple Humans
Shubhankar Borse, Seokeon Choi, Sunghyun Park +6
Generation of images containing multiple humans, performing complex actions, while preserving their facial identities, is a significant challenge. A major factor contributing to th…
RoCA: Robust Cross-Domain End-to-End Autonomous Driving
Rajeev Yasarla, Shizhong Han, Hsin-Pai Cheng +7
End-to-end (E2E) autonomous driving has recently emerged as a new paradigm, offering significant potential. However, few studies have looked into the practical challenge of deploym…
Distilling Multi-modal Large Language Models for Autonomous Driving
Deepti Hegde, Rajeev Yasarla, Hong Cai +7
Autonomous driving demands safe motion planning, especially in critical "long-tail" scenarios. Recent end-to-end autonomous driving systems leverage large language models (LLMs) as…
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning
Risheek Garrepalli, Shweta Mahajan, Munawar Hayat +1
Diffusion models excel at generative modeling (e.g., text-to-image) but sampling requires multiple denoising network passes, limiting practicality. Efforts such as progressive dist…
Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters
Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi +9
In this paper, we propose Sparse High Rank Adapters (SHiRA) that directly finetune 1-2% of the base model weights while leaving others unchanged, thus, resulting in a highly sparse…
FouRA: Fourier Low Rank Adaptation
Shubhankar Borse, Shreya Kadambi, Nilesh Prasad Pandey +7
While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models, LoRA fine-tuned text-to-image diffusion models lack diversity in the generated imag…
SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations
Jamie Menjay Lin, Jisoo Jeong, Hong Cai +3
Optical flow estimation is crucial to a variety of vision tasks. Despite substantial recent advancements, achieving real-time on-device optical flow estimation remains a complex ch…
OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation
Jisoo Jeong, Hong Cai, Risheek Garrepalli +3
The scarcity of ground-truth labels poses one major challenge in developing optical flow estimation models that are both generalizable and robust. While current methods rely on dat…
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
Rajeev Yasarla, Manish Kumar Singh, Hong Cai +6
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimati…
Clockwork Diffusion: Efficient Generation With Model-Step Distillation
Amirhossein Habibian, Amir Ghodrati, Noor Fathima +4
This work aims to improve the efficiency of text-to-image diffusion models. While diffusion models use computationally expensive UNet-based denoising operations in every generation…
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
Rajeev Yasarla, Hong Cai, Jisoo Jeong +3
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation. MAMo can augment and improve any single-image depth estimation networks into video de…
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow
Risheek Garrepalli, Jisoo Jeong, Rajeswaran C Ravindran +2
Recent advancements in neural network-based optical flow estimation often come with prohibitively high computational and memory requirements, presenting challenges in their model a…