no code implementations • 2 Feb 2025 • Haodi Xu, Joshua Fan, Feng Tao, Lifen Jiang, Fengqi You, Benjamin Z. Houlton, Ying Sun, Carla P. Gomes, Yiqi Luo
Big data and the rapid development of artificial intelligence (AI) provide unprecedented opportunities to enhance our understanding of the global carbon cycle and other biogeochemical processes.
1 code implementation • 13 Jan 2025 • Bangchen Yin, Jiaao Wang, Weitao Du, Pengbo Wang, Penghua Ying, Haojun Jia, Zisheng Zhang, Yuanqi Du, Carla P. Gomes, Chenru Duan, Hai Xiao, Graeme Henkelman
We present AlphaNet, a local frame-based equivariant model designed to achieve both accurate and efficient simulations for atomistic systems.
1 code implementation • 10 Oct 2024 • Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov
Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an exponentially large space of trajectories.
no code implementations • 8 Jul 2024 • Jin Peng Zhou, Christian K. Belardi, Ruihan Wu, Travis Zhang, Carla P. Gomes, Wen Sun, Kilian Q. Weinberger
Developing prompt-based methods with Large Language Models (LLMs) requires making numerous decisions, which give rise to a combinatorial search problem.
no code implementations • 20 Apr 2024 • Chenru Duan, Guan-Horng Liu, Yuanqi Du, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik
The RMSD and barrier height error is further improved by roughly 25\% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB.
no code implementations • 29 Mar 2024 • Yimeng Min, Carla P. Gomes
Our investigation explores how different training instance sizes, embedding dimensions, and distributions influence the outcomes of Unsupervised Learning methods.
no code implementations • 28 Feb 2024 • Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Dongxia Wu, Haorui Wang, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang
To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.
1 code implementation • 15 Aug 2023 • Ming-Chiang Chang, Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Lan Zhou, John M. Gregoire, Carla P. Gomes, R. Bruce van Dover, Michael O. Thompson
X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery processes.
no code implementations • 9 Jul 2023 • Hector Zenil, Jesper Tegnér, Felipe S. Abrahão, Alexander Lavin, Vipin Kumar, Jeremy G. Frey, Adrian Weller, Larisa Soldatova, Alan R. Bundy, Nicholas R. Jennings, Koichi Takahashi, Lawrence Hunter, Saso Dzeroski, Andrew Briggs, Frederick D. Gregory, Carla P. Gomes, Jon Rowe, James Evans, Hiroaki Kitano, Ross King
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
1 code implementation • NeurIPS 2023 • Yimeng Min, Yiwei Bai, Carla P. Gomes
Our loss function consists of two parts: one pushes the model to find the shortest path and the other serves as a surrogate for the constraint that the route should form a Hamiltonian Cycle.
no code implementations • 20 Sep 2022 • Dieqiao Feng, Carla P. Gomes, Bart Selman
We propose a domain-independent method that augments graph search with graph value iteration to solve hard planning instances that are out of reach for domain-specialized solvers.
no code implementations • 16 Jul 2022 • Joshua Fan, Di Chen, Jiaming Wen, Ying Sun, Carla P. Gomes
This poses a challenging coarsely-supervised regression (or downscaling) task; at training time, we only have SIF labels at a coarse resolution (3km), but we want to predict SIF at much finer spatial resolutions (e. g. 30m, a 100x increase).
1 code implementation • 2 Dec 2021 • Junwen Bai, Shufeng Kong, Carla P. Gomes
We find that by using contrastive learning in the supervised setting, we can exploit label information effectively in a data-driven manner, and learn meaningful feature and label embeddings which capture the label correlations and enhance the predictive power.
1 code implementation • 17 Nov 2021 • Joshua Fan, Junwen Bai, Zhiyun Li, Ariel Ortiz-Bobea, Carla P. Gomes
As far as we know, this is the first machine learning method that embeds geographical knowledge in crop yield prediction and predicts the crop yields at county level nationwide.
no code implementations • ICLR 2022 • Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger
In this paper, we investigate causes for this perceived instability.
no code implementations • NeurIPS 2020 • Dieqiao Feng, Carla P. Gomes, Bart Selman
In particular, as the size of the instance pool increases, the ``hardness gap'' decreases, which facilitates a smoother automated curriculum based learning process.
no code implementations • 21 Aug 2021 • Di Chen, Yiwei Bai, Sebastian Ament, Wenting Zhao, Dan Guevarra, Lan Zhou, Bart Selman, R. Bruce van Dover, John M. Gregoire, Carla P. Gomes
DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization.
1 code implementation • 8 Jun 2021 • John Paul Ryan, Sebastian Ament, Carla P. Gomes, Anil Damle
Kernel methods are a highly effective and widely used collection of modern machine learning algorithms.
2 code implementations • 4 Jun 2021 • Shufeng Kong, Dan Guevarra, Carla P. Gomes, John M. Gregoire
To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning.
BIG-bench Machine Learning
Generative Adversarial Network
+5
no code implementations • NeurIPS 2021 • Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger
In this paper we investigate how RL agents are affected by exchanging the small MLPs with larger modern networks with skip connections and normalization, focusing specifically on actor-critic algorithms.
no code implementations • 26 Feb 2021 • Johan Bjorck, Xiangyu Chen, Christopher De Sa, Carla P. Gomes, Kilian Q. Weinberger
Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning.
no code implementations • 5 Feb 2021 • Yiwei Bai, Wenting Zhao, Carla P. Gomes
There has been an increasing interest in harnessing deep learning to tackle combinatorial optimization (CO) problems in recent years.
no code implementations • 19 Jan 2021 • Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Ming-Chiang Chang, Dan Guevarra, Aine B. Connolly, John M. Gregoire, Michael O. Thompson, Carla P. Gomes, R. Bruce van Dover
Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery.
no code implementations • 30 Oct 2020 • Shufeng Kong, Junwen Bai, Jae Hee Lee, Di Chen, Andrew Allyn, Michelle Stuart, Malin Pinsky, Katherine Mills, Carla P. Gomes
A key problem in computational sustainability is to understand the distribution of species across landscapes over time.
1 code implementation • 4 Jun 2020 • Dieqiao Feng, Carla P. Gomes, Bart Selman
Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems.
no code implementations • 17 Oct 2019 • Di Chen, Yada Zhu, Xiaodong Cui, Carla P. Gomes
Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning losses, but are critical for decision making.
no code implementations • 25 Sep 2019 • Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving pattern de-mixing problems, typically in an unsupervised or weakly-supervised setting.
no code implementations • 3 Jun 2019 • Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes
At a high level, DRNets encode a structured latent space of the input data, which is constrained to adhere to prior knowledge by a reasoning module.
no code implementations • 25 Feb 2019 • Johan Bjorck, Brendan H. Rappazzo, Di Chen, Richard Bernstein, Peter H. Wrege, Carla P. Gomes
In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa.
no code implementations • 1 Nov 2018 • Di Chen, Carla P. Gomes
Citizen science projects are successful at gathering rich datasets for various applications.
no code implementations • ICML 2018 • Di Chen, Yexiang Xue, Carla P. Gomes
The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities.
no code implementations • 17 Sep 2017 • Luming Tang, Yexiang Xue, Di Chen, Carla P. Gomes
Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities.
no code implementations • 23 May 2017 • Xiaojian Wu, Yexiang Xue, Bart Selman, Carla P. Gomes
In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated.
no code implementations • NeurIPS 2016 • Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman
Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them.
1 code implementation • 3 Oct 2016 • Yexiang Xue, Junwen Bai, Ronan Le Bras, Brendan Rappazzo, Richard Bernstein, Johan Bjorck, Liane Longpre, Santosh K. Suram, Robert B. van Dover, John Gregoire, Carla P. Gomes
A key problem in materials discovery, the phase map identification problem, involves the determination of the crystal phase diagram from the materials' composition and structural characterization data.
no code implementations • 17 Aug 2015 • Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman
The ability to represent complex high dimensional probability distributions in a compact form is one of the key insights in the field of graphical models.
no code implementations • NeurIPS 2013 • Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
We consider the problem of sampling from a probability distribution defined over a high-dimensional discrete set, specified for instance by a graphical model.
no code implementations • 26 Sep 2013 • Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
Many probabilistic inference tasks involve summations over exponentially large sets.
no code implementations • NeurIPS 2012 • Stefano Ermon, Ashish Sabharwal, Bart Selman, Carla P. Gomes
Given a probabilistic graphical model, its density of states is a function that, for any likelihood value, gives the number of configurations with that probability.
no code implementations • NeurIPS 2011 • Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function.