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07/06/2023
When Does Confidence-Based Cascade Deferral Suffice?
Cascades are a classical strategy to enable inference cost to vary adapt...
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05/13/2023
Depth Dependence of μP Learning Rates in ReLU MLPs
In this short note we consider random fully connected ReLU networks of w...
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02/03/2023
ResMem: Learn what you can and memorize the rest
The impressive generalization performance of modern neural networks is a...
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01/30/2023
On student-teacher deviations in distillation: does it pay to disobey?
Knowledge distillation has been widely-used to improve the performance o...
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01/29/2023
Learning to reject meets OOD detection: Are all abstentions created equal?
Learning to reject (L2R) and out-of-distribution (OOD) detection are two...
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01/28/2023
Supervision Complexity and its Role in Knowledge Distillation
Despite the popularity and efficacy of knowledge distillation, there is ...
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01/28/2023
Leveraging Importance Weights in Subset Selection
We present a subset selection algorithm designed to work with arbitrary ...
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01/27/2023
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Large neural models (such as Transformers) achieve state-of-the-art perf...
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01/04/2023
Automating Nearest Neighbor Search Configuration with Constrained Optimization
The approximate nearest neighbor (ANN) search problem is fundamental to ...
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11/09/2022
Large Language Models with Controllable Working Memory
Large language models (LLMs) have led to a series of breakthroughs in na...
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11/01/2022
Preserving In-Context Learning ability in Large Language Model Fine-tuning
Pretrained large language models (LLMs) are strong in-context learners t...
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10/28/2022
When does mixup promote local linearity in learned representations?
Mixup is a regularization technique that artificially produces new sampl...
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10/12/2022
Large Models are Parsimonious Learners: Activation Sparsity in Trained Transformers
This paper studies the curious phenomenon for machine learning models wi...
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10/11/2022
Decoupled Context Processing for Context Augmented Language Modeling
Language models can be augmented with a context retriever to incorporate...
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06/28/2022
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s
This paper presents a novel nearest neighbor search algorithm achieving ...
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04/27/2022
ELM: Embedding and Logit Margins for Long-Tail Learning
Long-tail learning is the problem of learning under skewed label distrib...
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02/15/2022
Predicting on the Edge: Identifying Where a Larger Model Does Better
Much effort has been devoted to making large and more accurate models, b...
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02/02/2022
Robust Training of Neural Networks using Scale Invariant Architectures
In contrast to SGD, adaptive gradient methods like Adam allow robust tra...
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10/19/2021
When in Doubt, Summon the Titans: Efficient Inference with Large Models
Scaling neural networks to "large" sizes, with billions of parameters, h...
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07/29/2021
Batch Active Learning at Scale
The ability to train complex and highly effective models often requires ...
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06/19/2021
Teacher's pet: understanding and mitigating biases in distillation
Knowledge distillation is widely used as a means of improving the perfor...
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05/25/2021
Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets
Hierarchical Agglomerative Clustering (HAC) is one of the oldest but sti...
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05/19/2021
Balancing Robustness and Sensitivity using Feature Contrastive Learning
It is generally believed that robust training of extremely large network...
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05/12/2021
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Negative sampling schemes enable efficient training given a large number...
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04/26/2021
Balancing Constraints and Submodularity in Data Subset Selection
Deep learning has yielded extraordinary results in vision and natural la...
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02/05/2021
On the Reproducibility of Neural Network Predictions
Standard training techniques for neural networks involve multiple source...
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12/01/2020
Modifying Memories in Transformer Models
Large Transformer models have achieved impressive performance in many na...
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10/23/2020
Coping with Label Shift via Distributionally Robust Optimisation
The label shift problem refers to the supervised learning setting where ...
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07/27/2020
Learning discrete distributions: user vs item-level privacy
Much of the literature on differential privacy focuses on item-level pri...
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06/08/2020
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Transformer networks use pairwise attention to compute contextual embedd...
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05/31/2020
Evaluations and Methods for Explanation through Robustness Analysis
Among multiple ways of interpreting a machine learning model, measuring ...
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05/21/2020
Why distillation helps: a statistical perspective
Knowledge distillation is a technique for improving the performance of a...
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04/23/2020
Doubly-stochastic mining for heterogeneous retrieval
Modern retrieval problems are characterised by training sets with potent...
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04/21/2020
Federated Learning with Only Positive Labels
We consider learning a multi-class classification model in the federated...
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04/11/2020
Robust Large-Margin Learning in Hyperbolic Space
Recently, there has been a surge of interest in representation learning ...
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03/05/2020
Does label smoothing mitigate label noise?
Label smoothing is commonly used in training deep learning models, where...
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02/29/2020
Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
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02/17/2020
Low-Rank Bottleneck in Multi-head Attention Models
Attention based Transformer architecture has enabled significant advance...
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02/10/2020
Pre-training Tasks for Embedding-based Large-scale Retrieval
We consider the large-scale query-document retrieval problem: given a qu...
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12/20/2019
Are Transformers universal approximators of sequence-to-sequence functions?
Despite the widespread adoption of Transformer models for NLP tasks, the...
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12/06/2019
Why ADAM Beats SGD for Attention Models
While stochastic gradient descent (SGD) is still the de facto algorithm ...
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10/21/2019
Learning to Learn by Zeroth-Order Oracle
In the learning to learn (L2L) framework, we cast the design of optimiza...
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09/20/2019
Online Hierarchical Clustering Approximations
Hierarchical clustering is a widely used approach for clustering dataset...
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08/27/2019
New Loss Functions for Fast Maximum Inner Product Search
Quantization based methods are popular for solving large scale maximum i...
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08/20/2019
AdaCliP: Adaptive Clipping for Private SGD
Privacy preserving machine learning algorithms are crucial for learning ...
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07/24/2019
Sampled Softmax with Random Fourier Features
The computational cost of training with softmax cross entropy loss grows...
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06/05/2019
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
Neural Ordinary Differential Equation (Neural ODE) has been proposed as ...
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04/19/2019
On the Convergence of Adam and Beyond
Several recently proposed stochastic optimization methods that have been...
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03/25/2019
Local Orthogonal Decomposition for Maximum Inner Product Search
Inverted file and asymmetric distance computation (IVFADC) have been suc...
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03/20/2019
