2 citations · 2 across the 5 of their papers we have counts for
6 papers
Optimizing Effective Training Time for Large-Scale Recommendation Systems
Mingming Ding, Ruilin Chen, Yuzhen Huang +29
Lifecycle overhead silently consumes accelerator capacity across large-scale recommendation training fleets. Our largest recommendation workloads process tens of billions train- in…
Adaptive Block Diffusion: Resolving Training-Inference Mismatch in Diffusion Language Models
Gagan Jain
Diffusion Language Models (DLMs) are typically trained under fixed context structures, restricting denoising to predetermined token subsets. This creates a mismatch between trainin…
Masked Generative Nested Transformers with Decode Time Scaling
Sahil Goyal, Debapriya Tula, Gagan Jain +3
Recent advances in visual generation have made significant strides in producing content of exceptional quality. However, most methods suffer from a fundamental problem - a bottlene…
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program
Arpan Dasgupta, Gagan Jain, Arun Suggala +3
Mobile health (mHealth) programs face a critical challenge in optimizing the timing of automated health information calls to beneficiaries. This challenge has been formulated as a…
Mixture of Nested Experts: Adaptive Processing of Visual Tokens
Gagan Jain, Nidhi Hegde, Aditya Kusupati +5
The visual medium (images and videos) naturally contains a large amount of information redundancy, thereby providing a great opportunity for leveraging efficiency in processing. Wh…
LookupViT: Compressing visual information to a limited number of tokens
Rajat Koner, Gagan Jain, Prateek Jain +2
Vision Transformers (ViT) have emerged as the de-facto choice for numerous industry grade vision solutions. But their inference cost can be prohibitive for many settings, as they c…