research
∙
08/07/2023
AgentBench: Evaluating LLMs as Agents
Large Language Models (LLMs) are becoming increasingly smart and autonom...
share
research
∙
06/13/2023
SqueezeLLM: Dense-and-Sparse Quantization
Generative Large Language Models (LLMs) have demonstrated remarkable res...
research
∙
06/02/2023
Towards Robust GAN-generated Image Detection: a Multi-view Completion Representation
GAN-generated image detection now becomes the first line of defense agai...
research
∙
05/24/2023
Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts
The explosive growth of language models and their applications have led ...
research
∙
05/01/2023
Poisoning Language Models During Instruction Tuning
Instruction-tuned LMs such as ChatGPT, FLAN, and InstructGPT are finetun...
research
∙
03/13/2023
Scaling Vision-Language Models with Sparse Mixture of Experts
The field of natural language processing (NLP) has made significant stri...
research
∙
12/31/2022
New Challenges in Reinforcement Learning: A Survey of Security and Privacy
Reinforcement learning (RL) is one of the most important branches of AI....
research
∙
11/21/2022
Multitask Vision-Language Prompt Tuning
Prompt Tuning, conditioning on task-specific learned prompt vectors, has...
research
∙
11/03/2022
Crosslingual Generalization through Multitask Finetuning
Multitask prompted finetuning (MTF) has been shown to help large languag...
research
∙
10/27/2022
What Language Model to Train if You Have One Million GPU Hours?
The crystallization of modeling methods around the Transformer architect...
research
∙
10/17/2022
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution
Nowadays, online screen sharing and remote cooperation are becoming ubiq...
research
∙
04/20/2022
K-LITE: Learning Transferable Visual Models with External Knowledge
Recent state-of-the-art computer vision systems are trained from natural...
research
∙
03/13/2022
One Parameter Defense – Defending against Data Inference Attacks via Differential Privacy
Machine learning models are vulnerable to data inference attacks, such a...
research
∙
03/11/2022
Staged Training for Transformer Language Models
The current standard approach to scaling transformer language models tra...
research
∙
12/12/2021
Implicit Transformer Network for Screen Content Image Continuous Super-Resolution
Nowadays, there is an explosive growth of screen contents due to the wid...
research
∙
10/15/2021
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero...
research
∙
09/08/2021
What's Hidden in a One-layer Randomly Weighted Transformer?
We demonstrate that, hidden within one-layer randomly weighted neural ne...
research
∙
07/13/2021
How Much Can CLIP Benefit Vision-and-Language Tasks?
Most existing Vision-and-Language (V L) models rely on pre-trained vis...
research
∙
07/02/2021
Learned Token Pruning for Transformers
A major challenge in deploying transformer models is their prohibitive i...
research
∙
05/30/2021
MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models
Pruning is an effective method to reduce the memory footprint and comput...
research
∙
12/30/2020
Reservoir Transformer
We demonstrate that transformers obtain impressive performance even when...
research
∙
10/19/2020
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security
Federated learning is an improved version of distributed machine learnin...
research
∙
10/12/2020
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding
Phrase localization is a task that studies the mapping from textual phra...
research
∙
09/15/2020
Noisy Self-Knowledge Distillation for Text Summarization
In this paper we apply self-knowledge distillation to text summarization...
research
∙
08/16/2020
Differentially Private Multi-Agent Planning for Logistic-like Problems
Planning is one of the main approaches used to improve agents' working e...
research
∙
06/01/2020
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
We introduce AdaHessian, a second order stochastic optimization algorith...
research
∙
03/17/2020
Rethinking Batch Normalization in Transformers
The standard normalization method for neural network (NN) models used in...
research
∙
02/26/2020
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Since hardware resources are limited, the objective of training deep lea...
research
∙
09/12/2019
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
Transformer based architectures have become de-facto models used for a r...
research
∙
04/02/2019
Pragmatically Informative Text Generation
We improve the informativeness of models for conditional text generation...
research
∙
11/01/2018
On the Generation of Medical Question-Answer Pairs
Question answering (QA) has achieved promising progress recently. Howeve...
research
∙
06/07/2018
Ermes: Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
Most existing sentiment analysis approaches heavily rely on a large amou...
research
∙
05/16/2017
