101 citations · 383 across the 24 of their papers we have counts for
11 papers · 1 filter
Matryoshka Quantization
Pranav Nair, Puranjay Datta, Jeff Dean +2
Quantizing model weights is critical for reducing the communication and inference costs of large models. However, quantizing models -- especially to low precisions like int4 or int…
SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks
Mohammadreza Salehi, Sachin Mehta, Aditya Kusupati +2
We introduce SHARCS for adaptive inference that takes into account the hardness of input samples. SHARCS can train a router on any transformer network, enabling the model to direct…
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval
Ramnath Kumar, Anshul Mittal, Nilesh Gupta +3
Dense embedding-based retrieval is widely used for semantic search and ranking. However, conventional two-stage approaches, involving contrastive embedding learning followed by app…
MatFormer: Nested Transformer for Elastic Inference
Devvrit, Sneha Kudugunta, Aditya Kusupati +8
Foundation models are applied in a broad spectrum of settings with different inference constraints, from massive multi-accelerator clusters to resource-constrained standalone mobil…
Neural Priming for Sample-Efficient Adaptation
Matthew Wallingford, Vivek Ramanujan, Alex Fang +5
We propose Neural Priming, a technique for adapting large pretrained models to distribution shifts and downstream tasks given few or no labeled examples. Presented with class names…
AdANNS: A Framework for Adaptive Semantic Search
Aniket Rege, Aditya Kusupati, Sharan Ranjit S +5
Web-scale search systems learn an encoder to embed a given query which is then hooked into an approximate nearest neighbor search (ANNS) pipeline to retrieve similar data points. T…
Matryoshka Representation Learning
Aditya Kusupati, Gantavya Bhatt, Aniket Rege +8
Learned representations are a central component in modern ML systems, serving a multitude of downstream tasks. When training such representations, it is often the case that computa…
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan +6
Learning binary representations of instances and classes is a classical problem with several high potential applications. In modern settings, the compression of high-dimensional ne…
Soft Threshold Weight Reparameterization for Learnable Sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani +4
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of maximizing prediction accuracy given an overall parameter budget. Existing methods rely on uniform…
Extreme Regression for Dynamic Search Advertising
Yashoteja Prabhu, Aditya Kusupati, Nilesh Gupta +1
This paper introduces a new learning paradigm called eXtreme Regression (XR) whose objective is to accurately predict the numerical degrees of relevance of an extremely large numbe…
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
Aditya Kusupati, Manish Singh, Kush Bhatia +3
This paper develops the FastRNN and FastGRNN algorithms to address the twin RNN limitations of inaccurate training and inefficient prediction. Previous approaches have improved acc…