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05/26/2023
Training Socially Aligned Language Models in Simulated Human Society
Social alignment in AI systems aims to ensure that these models behave a...
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02/17/2023
Massively Multilingual Shallow Fusion with Large Language Models
While large language models (LLM) have made impressive progress in natur...
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10/11/2022
Mind's Eye: Grounded Language Model Reasoning through Simulation
Successful and effective communication between humans and AI relies on a...
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04/05/2022
PaLM: Scaling Language Modeling with Pathways
Large language models have been shown to achieve remarkable performance ...
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12/14/2021
Co-training Transformer with Videos and Images Improves Action Recognition
In learning action recognition, models are typically pre-trained on obje...
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12/13/2021
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Scaling language models with more data, compute and parameters has drive...
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09/03/2021
Finetuned Language Models Are Zero-Shot Learners
This paper explores a simple method for improving the zero-shot learning...
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07/17/2021
BEDS-Bench: Behavior of EHR-models under Distributional Shift–A Benchmark
Machine learning has recently demonstrated impressive progress in predic...
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02/03/2021
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records
One important challenge of applying deep learning to electronic health r...
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10/22/2020
Learnability and Complexity of Quantum Samples
Given a quantum circuit, a quantum computer can sample the output distri...
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10/13/2020
Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown tha...
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07/10/2020
Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss
When training the parameters of a linear dynamical model, the gradient d...
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12/02/2019
Flow Contrastive Estimation of Energy-Based Models
This paper studies a training method to jointly estimate an energy-based...
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11/14/2019
Modelling EHR timeseries by restricting feature interaction
Time series data are prevalent in electronic health records, mostly in t...
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11/13/2019
Federated and Differentially Private Learning for Electronic Health Records
The use of collaborative and decentralized machine learning techniques s...
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10/24/2019
Capacity, Bandwidth, and Compositionality in Emergent Language Learning
Many recent works have discussed the propensity, or lack thereof, for em...
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09/20/2019
Learning an Adaptive Learning Rate Schedule
The learning rate is one of the most important hyper-parameters for mode...
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09/06/2019
Improved Patient Classification with Language Model Pretraining Over Clinical Notes
Clinical notes in electronic health records contain highly heterogeneous...
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06/11/2019
Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records
Effective modeling of electronic health records (EHR) is rapidly becomin...
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06/10/2019
Analyzing the Role of Model Uncertainty for Electronic Health Records
In medicine, both ethical and monetary costs of incorrect predictions ca...
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09/12/2018
An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation
Music relies heavily on self-reference to build structure and meaning. W...
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09/12/2018
Music Transformer
Music relies heavily on repetition to build structure and meaning. Self-...
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08/20/2018
Peptide-Spectra Matching from Weak Supervision
As in many other scientific domains, we face a fundamental problem when ...
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06/12/2018
Embedding Text in Hyperbolic Spaces
Natural language text exhibits hierarchical structure in a variety of re...
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03/01/2018
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Despite recent advances in training recurrent neural networks (RNNs), ca...
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01/24/2018
Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipa...
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01/23/2018
MaskGAN: Better Text Generation via Filling in the ______
Neural text generation models are often autoregressive language models o...
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10/23/2017
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
Generative adversarial networks (GANs) are a family of generative models...
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03/26/2017
Who Said What: Modeling Individual Labelers Improves Classification
Data are often labeled by many different experts with each expert only l...
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05/25/2016
Adversarial Training Methods for Semi-Supervised Text Classification
Adversarial training provides a means of regularizing supervised learnin...
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11/19/2015
Generating Sentences from a Continuous Space
The standard recurrent neural network language model (RNNLM) generates s...
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11/04/2015
Semi-supervised Sequence Learning
We present two approaches that use unlabeled data to improve sequence le...
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07/29/2015
Document Embedding with Paragraph Vectors
Paragraph Vectors has been recently proposed as an unsupervised method f...
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12/17/2014
