66 citations · 101 across the 13 of their papers we have counts for
14 papers
AutoResearch at Production Scale: Failure Modes and a Multi-Agent Framework
Aparajith Chandran, Juwon Kim, Saurav Jha +2
Optimizing embedding systems for production recommendation pipelines demands systematic exploration that consumes disproportionate engineering effort at scale. We apply Andrej Karp…
Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
Nilaksh, Saurav Jha, Artem Zholus +1
World model-based policy evaluation is a practical proxy for testing real-world robot control by rolling out candidate actions in action-conditioned video diffusion models. As thes…
REAM: Merging Improves Pruning of Experts in LLMs
Saurav Jha, Maryam Hashemzadeh, Ali Saheb Pasand +3
Mixture-of-Experts (MoE) large language models (LLMs) are among the top-performing architectures. The largest models, often with hundreds of billions of parameters, pose significan…
Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models
Saurav Jha, Shiqi Yang, Masato Ishii +7
Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However,…
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models
Saurav Jha, Dong Gong, Lina Yao
Continual learning (CL) aims to help deep neural networks learn new knowledge while retaining what has been learned. Owing to their powerful generalizability, pre-trained vision-la…
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning
Yun Li, Zhe Liu, Saurav Jha +2
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects. In OW-CZSL, methods built on the conventional closed-world…
The Neural Process Family: Survey, Applications and Perspectives
Saurav Jha, Dong Gong, Xuesong Wang +2
The standard approaches to neural network implementation yield powerful function approximation capabilities but are limited in their abilities to learn meta representations and rea…
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
Francesco Pelosin, Saurav Jha, Andrea Torsello +2
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the k…
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation
Saurav Jha
Automatic Speech Recognition (ASR) systems have been gaining popularity in the recent years for their widespread usage in smart phones and speakers. Building ASR systems for task-s…
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis
Saurav Jha, Martin Schiemer, Franco Zambonelli +1
Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world a…
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization
Saurav Jha, Martin Schiemer, Juan Ye
Given the growing trend of continual learning techniques for deep neural networks focusing on the domain of computer vision, there is a need to identify which of these generalizes…
Learning cross-lingual phonological and orthagraphic adaptations: a case study in improving neural machine translation between low-resource languages
Saurav Jha, Akhilesh Sudhakar, Anil Kumar Singh
Out-of-vocabulary (OOV) words can pose serious challenges for machine translation (MT) tasks, and in particular, for low-resource language (LRL) pairs, i.e., language pairs for whi…
Multi Task Deep Morphological Analyzer: Context Aware Joint Morphological Tagging and Lemma Prediction
Saurav Jha, Akhilesh Sudhakar, Anil Kumar Singh
The ambiguities introduced by the recombination of morphemes constructing several possible inflections for a word makes the prediction of syntactic traits in Morphologically Rich L…
Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems
Saurav Jha, Nikhil Agarwal, Suneeta Agarwal
Given the recent deep learning advancements in face detection and recognition techniques for human faces, this paper answers the question "how well would they work for cartoons'?"…