5 citations · 13 across the 11 of their papers we have counts for
11 papers
Architecture-Sensitive Supervised Fine-Tuning for Screen-Conditioned Action Prediction: A PiSAR Benchmark
Rahul Bissa, Abhishek Vyas, Yash Jain
We benchmark three supervised fine-tuned models against frontier zero-shot baselines on a 661-row held-out slice of PiSAR (Persona, intent, Screen, Action, Rationale), a 12,929-tup…
Understanding Depth and Height Perception in Large Visual-Language Models
Shehreen Azad, Yash Jain, Rishit Garg +2
Geometric understanding - including depth and height perception - is fundamental to intelligence and crucial for navigating our environment. Despite the impressive capabilities of…
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition
Yash Jain, David Chan, Pranav Dheram +4
Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized mode…
PEEKABOO: Interactive Video Generation via Masked-Diffusion
Yash Jain, Anshul Nasery, Vibhav Vineet +1
Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However, a significant limitation is their inability to offer interactiv…
PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-Off
Sachit Kuhar, Yash Jain, Alexey Tumanov
Efficient inference of Deep Neural Networks (DNNs) on resource-constrained edge devices is essential. Quantization and sparsity are key techniques that translate to repetition and…
DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets
Yash Jain, Harkirat Behl, Zsolt Kira +1
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-specifi…
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data
Zikang Leng, Yash Jain, Hyeokhyen Kwon +1
Previous work has demonstrated that virtual accelerometry data, extracted from videos using cross-modality transfer approaches like IMUTube, is beneficial for training complex and…
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition
Yash Jain, Chi Ian Tang, Chulhong Min +2
A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is…
Matching Models for Graph Retrieval
Chitrank Gupta, Yash Jain
Graph Retrieval has witnessed continued interest and progress in the past few years. In thisreport, we focus on neural network based approaches for Graph matching and retrieving si…
Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracy
Chitrank Gupta, Yash Jain, Abir De +1
In recent years, inductive graph embedding models, \emph{viz.}, graph neural networks (GNNs) have become increasingly accurate at link prediction (LP) in online social networks. Th…
A novel alternative to analyzing multiple choice questions via discrimination index
P. K. Joshi, Y. Jain, R. Khunyakari +1
The value of multiple choice questions (MCQs) in seeking large-scale, high-stakes, educational assessment is widely established. Students' responses to test items with a multiple-c…