Code and datasets

AlexaTM 20B is a 20B-Parameter sequence-to-sequence transformer model created by the Alexa Teacher Model (AlexaTM) team at Amazon. The model was trained on a mixture of Common Crawl (mC4) and Wikipedia data across 12 languages using denoising and Causal Language Modeling (CLM) tasks. AlexaTM 20B can be used for in-context learning. "In-context learning," also known as "prompting," refers to a method for

Project Description This is a test framework for the bias bounties project. Getting Started as a Bounty Hunter If you are interacting with this codebase as a "bounty hunter", you'll need to have a way to run Jupyter notebooks. The easiest way to do this is to download Anaconda, which will also manage all of your python packages for you. See here for installation instructions: https://docs.anaconda.com/anaconda

This is a modified version of the original CaDiCaL repository on GitHub. We are releasing this version of CaDiCaL as a fork of the original CaDiCaL repository because this is preferred by the original author, Armin Biere. The version of CaDiCaL presented here can store the most relevant parts of its state (irredundant clauses, redundant clauses, and the reconstruction stack) in files, and it can also read

Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive to allow exploration. We propose an algorithm for batch RL, where effective policies are learned using only a fixed offline dataset instead of online interactions with the environment. The limited data in batch RL produces inherent

This module implements a set of functions to perform MAXENT from a causal perspective. The code here can be used to reproduce the results in the publication Obtaining Causal Information by Merging Datasets with MAXENT. The parts of the plots using KCI are, unfortunately, not available. To reproduce the results in the article create a python 3.6+ environment, pip install all the requirements.txt file and

Combinatorial optimization problems are pervasive across science and industry. Modern deep learning tools are poised to solve these problems at unprecedented scales, but a unifying framework that incorporates insights from statistical physics is still outstanding. Here we demonstrate how graph neural networks can be used to solve combinatorial optimization problems. Our approach is broadly applicable to

This repository contains the files for the blogpost "Detect adversarial inputs using Amazon SageMaker Model Monitor and Amazon SageMaker Debugger". Create a SageMaker notebook instance and clone the repository: git clone https://github.com/amazon-research/detecting-adversarial-samples-using-sagemaker.git In the notebook Detecting_adversarial_samples.ipynb we first train an image classification model (ResNet18

As training deep learning models on large dataset takes a lot of time and resources, it is desired to construct a small synthetic dataset with which we can train deep learning models sufficiently. There are recent works that have explored solutions on condensing image datasets through complex bi-level optimization. For instance, dataset condensation (DC) matches network gradients w.r.t. large-real data

Large-scale pre-trained sequence-to-sequence models like BART and T5 achieve state-of-the-art performance on many generative NLP tasks. However, such models pose a great challenge in resource-constrained scenarios owing to their large memory requirements and high latency. To alleviate this issue, we propose to jointly distill and quantize the model, where knowledge is transferred from the full-precision

This repository contains the code for the paper: "Learning dialogue representations from consecutive utterances" (NAACL 2022).

This repo is the official implementation of "Earthformer: Exploring space-time transformers for earth system forecasting" that appeared in NeurIPS 2022.

Our datasets cover binary, multi-class, and multilabel classification tasks and represent various ways information is organized in a long text (e.g. information that is critical to making the classification decision is at the beginning or toward the end of the document). Our results show that more complex models often fail to outperform simple baselines and yield inconsistent performance across datasets