307 citations · 1.8k across the 219 of their papers we have counts for
13 papers · 1 filter
Defining and Generating Compositional Structure
Eric Elmoznino, Tejas Kasetty, Blaise Agüera y Arcas +2
Compositional structure is pervasive in nature and across scales: atoms combine into molecules, molecules into cells, and cells into organisms, while further compositional hierarch…
The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale
Judah Goldfeder, Ayush Rai, Philippe M. Wyder +11
Buildings cause nearly 40\% of carbon emissions in the US. Of total building emissions, HVAC systems are responsible for approximately 40-60\% and account for 15\% of global energy…
Mixture of GFlowNets
Omar G. Younis, Joseph D. Viviano, Chirayu Anant Haryan +3
Generative Flow Networks (GFlowNets) learn a policy that samples compositional objects in proportion to an unnormalized reward . This makes them useful wherever many diverse,…
Multi-Objective Amortized CDRH3 Sequence Sampling Diversifies Promising Antibody Candidates
Joseph D. Viviano, Almer van der Sloot, Quentin Fournier +3
Antibody therapeutics have transformed treatment across diverse therapeutic areas and have become central to modern medicine. While recent machine learning approaches aim to accele…
Bayesian Symbolic Regression with Entropic Reinforcement Learning
Oussama Boussif, Mohammed Mahfoud, Younesse Kaddar +6
Symbolic regression is the problem of finding an algebraic expression describing a stochastic dependence of a target variable on a set of inputs. Unlike forms of regression that fi…
Autoregressive Boltzmann Generators
Danyal Rehman, Charlie B. Tan, Yoshua Bengio +2
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generato…
Adaptive Order Policies for Masked Diffusion
Jama Hussein Mohamud, Mohsin Hasan, Mirco Ravanelli +1
Masked diffusion models have seen great success in capturing data distributions over discrete sequences in domains such as text and proteins. These models generate data by iterativ…
Generative Recursive Reasoning
Junyeob Baek, Mingyu Jo, Minsu Kim +3
How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising alternative to autoregressive sequence extension by p…
Language models recognize dropout and Gaussian noise applied to their activations
Damiano Fornasiere, Mirko Bronzi, Spencer Kitts +3
We provide evidence that language models can detect, localize and, to a degree, verbalize the difference between a-semantic perturbations applied to their activations. More precise…
Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models
Oliver E. Richardson, Mandana Samiei, Mehran Shakerinava +4
We present a generic algorithm for learning and approximate inference with an intuitive epistemic interpretation: iteratively focus on a subset of the model and resolve inconsisten…
General Multimodal Protein Design Enables DNA-Encoding of Chemistry
Jarrid Rector-Brooks, Théophile Lambert, Marta Skreta +15
Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new…
Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
Hyeonah Kim, Minsu Kim, Celine Roget +5
The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in pract…
Discrete Feynman-Kac Correctors
Mohsin Hasan, Viktor Ohanesian, Artem Gazizov +5
Discrete diffusion models have recently emerged as a promising alternative to the autoregressive approach for generating discrete sequences. Sample generation via gradual denoising…