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Found 11 papers, 10 papers with code
Likelihood-Based Diffusion Language Models
On the algorithmic front, we introduce several methodological improvements for the maximum-likelihood training of diffusion language models.
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
As a demonstration of the research possible in AlpacaFarm, we find that methods that use a reward model can substantially improve over supervised fine-tuning and that our reference PPO implementation leads to a +10% improvement in win-rate against Davinci003.
Diffusion-LM Improves Controllable Text Generation
Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation.
In Search of Lost Domain Generalization
As a first step, we realize that model selection is non-trivial for domain generalization tasks.
Ranked #49 on
Domain Generalization
on PACS
Towards GAN Benchmarks Which Require Generalization
For many evaluation metrics commonly used as benchmarks for unconditional image generation, trivially memorizing the training set attains a better score than models which are considered state-of-the-art; we consider this problematic.
Invariant Risk Minimization
We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions.
GANSynth: Adversarial Neural Audio Synthesis
Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence.
Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.
Ranked #3 on
Image Generation
on CAT 256x256
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.
Ranked #1 on
Speech Synthesis
on Blizzard Challenge 2013
PixelVAE: A Latent Variable Model for Natural Images
Natural image modeling is a landmark challenge of unsupervised learning.
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
Ranked #66 on
Sentiment Analysis
on SST-2 Binary classification
