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Found 11 papers, 10 papers with code
Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic Images in Facial Capture Pipelines
We propose an end-to-end pipeline for both building and tracking 3D facial models from personalized in-the-wild (cellphone, webcam, youtube clips, etc.)
Parameter-Efficient Transfer Learning with Diff Pruning
This approach views finetuning as learning a task-specific diff vector that is applied on top of the pretrained parameter vector, which remains fixed and is shared across different tasks.
Sequence-Level Mixed Sample Data Augmentation
Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation.
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
To this end, we select a suite of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes.
MicroNet for Efficient Language Modeling
In this paper, we provide the winning solution to the NeurIPS 2019 MicroNet Challenge in the language modeling track.
Analyzing machine-learned representations: A natural language case study
Analyzing performance on these diagnostic tests indicates a lack of systematicity in the representations and decision rules, and reveals a set of heuristic strategies.
Why Build an Assistant in Minecraft?
In this document we describe a rationale for a research program aimed at building an open "assistant" in the game Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue.
CraftAssist: A Framework for Dialogue-enabled Interactive Agents
This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions.
Latent Alignment and Variational Attention
This work considers variational attention networks, alternatives to soft and hard attention for learning latent variable alignment models, with tighter approximation bounds based on amortized variational inference.
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Evaluating Compositionality in Sentence Embeddings
Further, we find that augmenting training with our dataset improves test performance on our dataset without loss of performance on the original training dataset.
