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08/17/2023
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Whether current or near-term AI systems could be conscious is a topic of...
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07/11/2023
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
The practical utility of causality in decision-making is widespread and ...
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07/10/2023
International Institutions for Advanced AI
International institutions may have an important role to play in ensurin...
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07/07/2023
Simulation-free Schrödinger bridges via score and flow matching
We present simulation-free score and flow matching ([SF]^2M), a simulati...
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07/04/2023
Generative Flow Networks: a Markov Chain Perspective
While Markov chain Monte Carlo methods (MCMC) provide a general framewor...
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06/30/2023
Thompson sampling for improved exploration in GFlowNets
Generative flow networks (GFlowNets) are amortized variational inference...
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06/27/2023
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
Genomic (DNA) sequences encode an enormous amount of information for gen...
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06/26/2023
BatchGFN: Generative Flow Networks for Batch Active Learning
We introduce BatchGFN – a novel approach for pool-based active learning ...
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06/21/2023
Constant Memory Attention Block
Modern foundation model architectures rely on attention mechanisms to ef...
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06/20/2023
Multi-Fidelity Active Learning with GFlowNets
In the last decades, the capacity to generate large amounts of data in s...
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06/03/2023
Cycle Consistency Driven Object Discovery
Developing deep learning models that effectively learn object-centric re...
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06/01/2023
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Solar power harbors immense potential in mitigating climate change by su...
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05/31/2023
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
The aim of object-centric vision is to construct an explicit representat...
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05/30/2023
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Generative Flow Networks (GFlowNets), a class of generative models over ...
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05/27/2023
Attention Schema in Neural Agents
Attention has become a common ingredient in deep learning architectures....
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05/26/2023
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Combinatorial optimization (CO) problems are often NP-hard and thus out ...
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05/24/2023
Model evaluation for extreme risks
Current approaches to building general-purpose AI systems tend to produc...
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05/23/2023
Constant Memory Attentive Neural Processes
Neural Processes (NPs) are efficient methods for estimating predictive u...
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02/21/2023
Hyena Hierarchy: Towards Larger Convolutional Language Models
Recent advances in deep learning have relied heavily on the use of large...
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02/21/2023
Reusable Slotwise Mechanisms
Agents that can understand and reason over the dynamics of objects can h...
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02/19/2023
Stochastic Generative Flow Networks
Generative Flow Networks (or GFlowNets for short) are a family of probab...
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02/13/2023
GFlowNet-EM for learning compositional latent variable models
Latent variable models (LVMs) with discrete compositional latents are an...
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02/13/2023
Sources of Richness and Ineffability for Phenomenally Conscious States
Conscious states (states that there is something it is like to be in) se...
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02/11/2023
Distributional GFlowNets with Quantile Flows
Generative Flow Networks (GFlowNets) are a new family of probabilistic s...
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02/08/2023
DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks
Learning the causal structure of observable variables is a central focus...
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02/03/2023
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov ...
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02/01/2023
GFlowNets for AI-Driven Scientific Discovery
Tackling the most pressing problems for humanity, such as the climate cr...
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02/01/2023
Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport
Continuous normalizing flows (CNFs) are an attractive generative modelin...
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01/30/2023
A theory of continuous generative flow networks
Generative flow networks (GFlowNets) are amortized variational inference...
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01/27/2023
Leveraging the Third Dimension in Contrastive Learning
Self-Supervised Learning (SSL) methods operate on unlabeled data to lear...
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12/27/2022
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Mixup is a popular data augmentation technique for training deep neural ...
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11/26/2022
Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective
Although disentangled representations are often said to be beneficial fo...
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11/22/2022
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Mitigating the climate crisis requires a rapid transition towards lower ...
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11/15/2022
Latent Bottlenecked Attentive Neural Processes
Neural Processes (NPs) are popular methods in meta-learning that can est...
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11/11/2022
Equivariance with Learned Canonicalization Functions
Symmetry-based neural networks often constrain the architecture in order...
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11/07/2022
Posterior samples of source galaxies in strong gravitational lenses with score-based priors
Inferring accurate posteriors for high-dimensional representations of th...
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11/04/2022
Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes
Bayesian causal structure learning aims to learn a posterior distributio...
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11/01/2022
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions
Generative Flow Networks (GFlowNets) have demonstrated significant perfo...
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11/01/2022
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (RL) is a promising direction fo...
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10/24/2022
GFlowOut: Dropout with Generative Flow Networks
Bayesian Inference offers principled tools to tackle many critical probl...
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10/23/2022
Multi-Objective GFlowNets
In many applications of machine learning, like drug discovery and materi...
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10/15/2022
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an important driver of progress in artificial...
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10/12/2022
Contrastive introspection (ConSpec) to rapidly identify invariant steps for success
Reinforcement learning (RL) algorithms have achieved notable success in ...
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10/11/2022
Robust and Controllable Object-Centric Learning through Energy-based Models
Humans are remarkably good at understanding and reasoning about complex ...
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10/11/2022
MAgNet: Mesh Agnostic Neural PDE Solver
The computational complexity of classical numerical methods for solving ...
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10/07/2022
Generative Augmented Flow Networks
The Generative Flow Network is a probabilistic framework where an agent ...
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10/04/2022
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning
In cooperative multi-agent reinforcement learning, a team of agents work...
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10/03/2022
Latent State Marginalization as a Low-cost Approach for Improving Exploration
While the maximum entropy (MaxEnt) reinforcement learning (RL) framework...
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10/02/2022
GFlowNets and variational inference
This paper builds bridges between two families of probabilistic algorith...
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09/26/2022