Search Results for author:
Found 19 papers, 12 papers with code
MatFormer: Nested Transformer for Elastic Inference
Furthermore, we observe that smaller encoders extracted from a universal MatFormer-based ViT (MatViT) encoder preserve the metric-space structure for adaptive large-scale retrieval.
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters.
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Standard Convolutional Neural Networks (CNNs) designed for computer vision tasks tend to have large intermediate activation maps.
Ranked #27 on
Face Detection
on WIDER Face (Medium)
Matryoshka Representation Learning
The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations.
Ranked #25 on
Image Classification
on ObjectNet
(using extra training data)
Soft Threshold Weight Reparameterization for Learnable Sparsity
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of maximizing prediction accuracy given an overall parameter budget.
AdANNS: A Framework for Adaptive Semantic Search
Finally, we demonstrate that AdANNS can enable inference-time adaptivity for compute-aware search on ANNS indices built non-adaptively on matryoshka representations.
FLUID: A Unified Evaluation Framework for Flexible Sequential Data
To foster research towards the goal of general ML methods, we introduce a new unified evaluation framework - FLUID (Flexible Sequential Data).
Are "Hierarchical" Visual Representations Hierarchical?
In this work, we set out to investigate if hierarchical visual representations truly capture the human perceived hierarchy better than standard learned representations.
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
We further quantitatively measure the quality of our codes by applying it to the efficient image retrieval as well as out-of-distribution (OOD) detection problems.
Neural Priming for Sample-Efficient Adaptation
Performing lightweight updates on the recalled data significantly improves accuracy across a variety of distribution shift and transfer learning benchmarks.
Neural Radiance Field Codebooks
Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks.
One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification
Edge sensing with micro-power pulse-Doppler radars is an emergent domain in monitoring and surveillance with several smart city applications.
Extreme Regression for Dynamic Search Advertising
This paper also introduces a (3) new labelwise prediction algorithm in XReg useful for DSA and other recommendation tasks.
Disrupting Model Training with Adversarial Shortcuts
When data is publicly released for human consumption, it is unclear how to prevent its unauthorized usage for machine learning purposes.
MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound
Given a video, we replace snippets of text and audio with a MASK token; the model learns by choosing the correct masked-out snippet.
Ranked #6 on
Action Classification
on Kinetics-600
(using extra training data)
ProtoSound: A Personalized and Scalable Sound Recognition System for Deaf and Hard-of-Hearing Users
Recent advances have enabled automatic sound recognition systems for deaf and hard of hearing (DHH) users on mobile devices.
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval
Such techniques use a two-stage process: (a) contrastive learning to train a dual encoder to embed both the query and documents and (b) approximate nearest neighbor search (ANNS) for finding similar documents for a given query.
SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks
We introduce SHARCS for adaptive inference that takes into account the hardness of input samples.
Gecko: Versatile Text Embeddings Distilled from Large Language Models
On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size.
