Search Results for author: Ben Krause

Found 13 papers, 8 papers with code

AutoGRAMS: Autonomous Graphical Agent Modeling Software

1 code implementation14 Jul 2024 Ben Krause, Lucia Chen, Emmanuel Kahembwe

We introduce the AutoGRAMS framework for programming multi-step interactions with language models.

Language Modelling

XGen-7B Technical Report

1 code implementation7 Sep 2023 Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, Senthil Purushwalkam, Tong Niu, Wojciech Kryściński, Lidiya Murakhovs'ka, Prafulla Kumar Choubey, Alex Fabbri, Ye Liu, Rui Meng, Lifu Tu, Meghana Bhat, Chien-Sheng Wu, Silvio Savarese, Yingbo Zhou, Shafiq Joty, Caiming Xiong

Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.

2k 8k

Don’t throw away that linear head: Few-shot protein fitness prediction with generative models

no code implementations29 Sep 2021 Ben Krause, Nikhil Naik, Wenhao Liu, Ali Madani

Predicting the fitness, i. e. functional value, of a protein sequence is an important and challenging task in biology, particularly due to the scarcity of assay-labeled data.

Transfer Learning

Deep Extrapolation for Attribute-Enhanced Generation

1 code implementation NeurIPS 2021 Alvin Chan, Ali Madani, Ben Krause, Nikhil Naik

Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution.

Attribute

Explaining and Improving Model Behavior with k Nearest Neighbor Representations

no code implementations18 Oct 2020 Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin, Tong Niu, Richard Socher, Caiming Xiong

Interpretability techniques in NLP have mainly focused on understanding individual predictions using attention visualization or gradient-based saliency maps over tokens.

Natural Language Inference

GeDi: Generative Discriminator Guided Sequence Generation

3 code implementations Findings (EMNLP) 2021 Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, Nazneen Fatema Rajani

While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate.

Attribute Linguistic Acceptability +1

Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width

no code implementations10 Feb 2020 Yu Bai, Ben Krause, Huan Wang, Caiming Xiong, Richard Socher

We propose \emph{Taylorized training} as an initiative towards better understanding neural network training at finite width.

Dynamic Evaluation of Transformer Language Models

1 code implementation17 Apr 2019 Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals

This research note combines two methods that have recently improved the state of the art in language modeling: Transformers and dynamic evaluation.

Language Modelling

Multiplicative LSTM for sequence modelling

1 code implementation26 Sep 2016 Ben Krause, Liang Lu, Iain Murray, Steve Renals

We introduce multiplicative LSTM (mLSTM), a recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures.

Density Estimation Language Modelling

Optimizing and Contrasting Recurrent Neural Network Architectures

no code implementations16 Oct 2015 Ben Krause

Recurrent Neural Networks (RNNs) have long been recognized for their potential to model complex time series.

Time Series Time Series Analysis

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