296 citations · 1.7k across the 43 of their papers we have counts for
46 papers
Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders
Benjamin Rozonoyer, Chong You, Michael Boratko +5
The success of Large Language Models (LLMs) has motivated a shift toward generative approaches to retrieval and ranking, aiming to supersede classical Dual Encoders (DEs) and Cross…
Scalable In-context Ranking with Generative Models
Nilesh Gupta, Chong You, Srinadh Bhojanapalli +3
In-context Ranking (ICR) is an emerging paradigm for Information Retrieval (IR), which leverages contextual understanding of LLMs by directly incorporating the task description, ca…
Spark Transformer: Reactivating Sparsity in FFN and Attention
Chong You, Kan Wu, Zhipeng Jia +16
The discovery of the lazy neuron phenomenon in trained Transformers, where the vast majority of neurons in their feed-forward networks (FFN) are inactive for each token, has spurre…
Mimetic Initialization Helps State Space Models Learn to Recall
Asher Trockman, Hrayr Harutyunyan, J. Zico Kolter +2
Recent work has shown that state space models such as Mamba are significantly worse than Transformers on recall-based tasks due to the fact that their state size is constant with r…
Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count
Hanseul Cho, Jaeyoung Cha, Srinadh Bhojanapalli +1
Transformers often struggle with length generalization, meaning they fail to generalize to sequences longer than those encountered during training. While arithmetic tasks are commo…
Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure
Hanseul Cho, Jaeyoung Cha, Pranjal Awasthi +3
Even for simple arithmetic tasks like integer addition, it is challenging for Transformers to generalize to longer sequences than those encountered during training. To tackle this…
Efficient Language Model Architectures for Differentially Private Federated Learning
Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu +2
Cross-device federated learning (FL) is a technique that trains a model on data distributed across typically millions of edge devices without data leaving the devices. SGD is the s…
HiRE: High Recall Approximate Top- Estimation for Efficient LLM Inference
Yashas Samaga B L, Varun Yerram, Chong You +4
Autoregressive decoding with generative Large Language Models (LLMs) on accelerators (GPUs/TPUs) is often memory-bound where most of the time is spent on transferring model paramet…
Dual-Encoders for Extreme Multi-Label Classification
Nilesh Gupta, Devvrit Khatri, Ankit S Rawat +3
Dual-encoder (DE) models are widely used in retrieval tasks, most commonly studied on open QA benchmarks that are often characterized by multi-class and limited training data. In c…
Functional Interpolation for Relative Positions Improves Long Context Transformers
Shanda Li, Chong You, Guru Guruganesh +7
Preventing the performance decay of Transformers on inputs longer than those used for training has been an important challenge in extending the context length of these models. Thou…
Depth Dependence of P Learning Rates in ReLU MLPs
Samy Jelassi, Boris Hanin, Ziwei Ji +3
In this short note we consider random fully connected ReLU networks of width and depth equipped with a mean-field weight initialization. Our purpose is to study the depende…
On student-teacher deviations in distillation: does it pay to disobey?
Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli +2
Knowledge distillation (KD) has been widely used to improve the test accuracy of a "student" network, by training it to mimic the soft probabilities of a trained "teacher" network.…
On the Adversarial Robustness of Mixture of Experts
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme +2
Adversarial robustness is a key desirable property of neural networks. It has been empirically shown to be affected by their sizes, with larger networks being typically more robust…
The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers
Zonglin Li, Chong You, Srinadh Bhojanapalli +8
This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse. By activation map we refer to the interm…
Treeformer: Dense Gradient Trees for Efficient Attention Computation
Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain +1
Standard inference and training with transformer based architectures scale quadratically with input sequence length. This is prohibitively large for a variety of applications espec…
Robust Training of Neural Networks Using Scale Invariant Architectures
Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer +2
In contrast to SGD, adaptive gradient methods like Adam allow robust training of modern deep networks, especially large language models. However, the use of adaptivity not only com…
Leveraging redundancy in attention with Reuse Transformers
Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit +5
Pairwise dot product-based attention allows Transformers to exchange information between tokens in an input-dependent way, and is key to their success across diverse applications i…
Teacher's pet: understanding and mitigating biases in distillation
Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon +1
Knowledge distillation is widely used as a means of improving the performance of a relatively simple student model using the predictions from a complex teacher model. Several works…
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation
Srinadh Bhojanapalli, Ayan Chakrabarti, Himanshu Jain +3
State-of-the-art transformer models use pairwise dot-product based self-attention, which comes at a computational cost quadratic in the input sequence length. In this paper, we inv…
A Simple and Effective Positional Encoding for Transformers
Pu-Chin Chen, Henry Tsai, Srinadh Bhojanapalli +3
Transformer models are permutation equivariant. To supply the order and type information of the input tokens, position and segment embeddings are usually added to the input. Recent…
Understanding Robustness of Transformers for Image Classification
Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner +3
Deep Convolutional Neural Networks (CNNs) have long been the architecture of choice for computer vision tasks. Recently, Transformer-based architectures like Vision Transformer (Vi…
On the Reproducibility of Neural Network Predictions
Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit +4
Standard training techniques for neural networks involve multiple sources of randomness, e.g., initialization, mini-batch ordering and in some cases data augmentation. Given that n…
Modifying Memories in Transformer Models
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer +4
Large Transformer models have achieved impressive performance in many natural language tasks. In particular, Transformer based language models have been shown to have great capabil…
An efficient nonconvex reformulation of stagewise convex optimization problems
Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli +2
Convex optimization problems with staged structure appear in several contexts, including optimal control, verification of deep neural networks, and isotonic regression. Off-the-she…