6 citations · 23 across the 19 of their papers we have counts for
20 papers
SkipKV: Selective Skipping of KV Generation and Storage for Efficient Inference with Large Reasoning Models
Jiayi Tian, Seyedarmin Azizi, Yequan Zhao +7
Large reasoning models (LRMs) often incur significant key-value (KV) cache overhead, due to their linear growth with the verbose chain-of-thought (CoT) reasoning. This incurs both…
Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration
Jucheng Shen, Gaurav Sarkar, Yeonju Ro +4
We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate the dynamic nature of token unmasking confide…
Harnessing Object Grounding for Time-Sensitive Video Understanding
Tz-Ying Wu, Sharath Nittur Sridhar, Subarna Tripathi
We propose to improve the time-sensitive video understanding (TSV) capability of video large language models (Video-LLMs) with grounded objects (GO). We hypothesize that TSV tasks…
Toward Scalable Video Narration: A Training-free Approach Using Multimodal Large Language Models
Tz-Ying Wu, Tahani Trigui, Sharath Nittur Sridhar +2
In this paper, we introduce VideoNarrator, a novel training-free pipeline designed to generate dense video captions that offer a structured snapshot of video content. These caption…
EASG-Bench: Video Q&A Benchmark with Egocentric Action Scene Graphs
Ivan Rodin, Tz-Ying Wu, Kyle Min +4
We introduce EASG-Bench, a question-answering benchmark for egocentric videos where the question-answering pairs are created from spatio-temporally grounded dynamic scene graphs ca…
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression
Souvik Kundu, Anahita Bhiwandiwalla, Sungduk Yu +6
Despite recent efforts in understanding the compression impact on large language models (LLMs) in terms of their downstream task performance and trustworthiness on relatively simpl…
LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models
Anthony Sarah, Sharath Nittur Sridhar, Maciej Szankin +1
The abilities of modern large language models (LLMs) in solving natural language processing, complex reasoning, sentiment analysis and other tasks have been extraordinary which has…
SimQ-NAS: Simultaneous Quantization Policy and Neural Architecture Search
Sharath Nittur Sridhar, Maciej Szankin, Fang Chen +2
Recent one-shot Neural Architecture Search algorithms rely on training a hardware-agnostic super-network tailored to a specific task and then extracting efficient sub-networks for…
InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning
Sharath Nittur Sridhar, Souvik Kundu, Sairam Sundaresan +2
One-Shot Neural Architecture Search (NAS) algorithms often rely on training a hardware agnostic super-network for a domain specific task. Optimal sub-networks are then extracted fr…
Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs
Souvik Kundu, Sairam Sundaresan, Sharath Nittur Sridhar +3
Existing deep neural networks (DNNs) that achieve state-of-the-art (SOTA) performance on both clean and adversarially-perturbed images rely on either activation or weight condition…
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities
Daniel Cummings, Anthony Sarah, Sharath Nittur Sridhar +3
Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific sup…
A Hardware-Aware System for Accelerating Deep Neural Network Optimization
Anthony Sarah, Daniel Cummings, Sharath Nittur Sridhar +4
Recent advances in Neural Architecture Search (NAS) which extract specialized hardware-aware configurations (a.k.a. "sub-networks") from a hardware-agnostic "super-network" have be…
Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms
Daniel Cummings, Sharath Nittur Sridhar, Anthony Sarah +1
Neural architecture search (NAS), the study of automating the discovery of optimal deep neural network architectures for tasks in domains such as computer vision and natural langua…
TrimBERT: Tailoring BERT for Trade-offs
Sharath Nittur Sridhar, Anthony Sarah, Sairam Sundaresan
Models based on BERT have been extremely successful in solving a variety of natural language processing (NLP) tasks. Unfortunately, many of these large models require a great deal…
Attention-based Image Upsampling
Souvik Kundu, Hesham Mostafa, Sharath Nittur Sridhar +1
Convolutional layers are an integral part of many deep neural network solutions in computer vision. Recent work shows that replacing the standard convolution operation with mechani…
Undivided Attention: Are Intermediate Layers Necessary for BERT?
Sharath Nittur Sridhar, Anthony Sarah
In recent times, BERT-based models have been extremely successful in solving a variety of natural language processing (NLP) tasks such as reading comprehension, natural language in…
Compact Scene Graphs for Layout Composition and Patch Retrieval
Subarna Tripathi, Sharath Nittur Sridhar, Sairam Sundaresan +1
Structured representations such as scene graphs serve as an efficient and compact representation that can be used for downstream rendering or retrieval tasks. However, existing eff…
Fast Weight Long Short-Term Memory
T. Anderson Keller, Sharath Nittur Sridhar, Xin Wang
Associative memory using fast weights is a short-term memory mechanism that substantially improves the memory capacity and time scale of recurrent neural networks (RNNs). As recent…
Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
Matthew Johnson-Roberson, Charles Barto, Rounak Mehta +3
Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon ma…
On Optimizing Human-Machine Task Assignments
Andreas Veit, Michael Wilber, Rajan Vaish +48
When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the cro…