Search Results for author:
Found 15 papers, 9 papers with code
Goal-Conditioned Reinforcement Learning in the Presence of an Adversary
Finally, we unify the two threads and introduce IGOAL: a novel framework for goal-conditioned learning in the presence of an adversary.
Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
We introduce a challenging decision-making task that we call active acquisition for multimodal temporal data (A2MT).
General-purpose, long-context autoregressive modeling with Perceiver AR
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression.
Ranked #35 on
Language Modelling
on WikiText-103
Structure-aware generation of drug-like molecules
Molecules are built atom-by-atom inside pockets, guided by structural information from crystallographic data.
Message Passing Neural Processes
Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity.
Generative Compositional Augmentations for Scene Graph Prediction
However, test images might contain zero- and few-shot compositions of objects and relationships, e. g. <cup, on, surfboard>.
Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks.
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
We show that such models can suffer the most in their ability to generalize to rare compositions, evaluating two different models on the Visual Genome dataset and its more recent, improved version, GQA.
Deep Graph Mapper: Seeing Graphs through the Neural Lens
Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties of a graph.
The PlayStation Reinforcement Learning Environment (PSXLE)
We propose a new benchmark environment for evaluating Reinforcement Learning (RL) algorithms: the PlayStation Learning Environment (PSXLE), a PlayStation emulator modified to expose a simple control API that enables rich game-state representations.
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task.
Spatio-Temporal Deep Graph Infomax
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time.
Structure-Based Networks for Drug Validation
Classifying chemicals according to putative modes of action (MOAs) is of paramount importance in the context of risk assessment.
Towards Sparse Hierarchical Graph Classifiers
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks.
XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification
Our work improves on existing multimodal deep learning algorithms in two essential ways: (1) it presents a novel method for performing cross-modality (before features are learned from individual modalities) and (2) extends the previously proposed cross-connections which only transfer information between streams that process compatible data.
