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Found 14 papers, 6 papers with code
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery.
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives.
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape.
ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis
We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature.
A GPT-4 Reticular Chemist for Guiding MOF Discovery
We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher.
Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes
We define a novel measure, which we call the "robust ensemble" (RE), which suppresses trapping by isolated configurations and amplifies the role of these dense regions.
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles.
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation
We show that the mean squared error (MSE) of our estimator converges to $0$ at the rate of $O(d^2 (pn)^{-2/5})$ as long as $\omega(d^5 n)$ random entries from a total of $n^2$ entries of $Y$ are observed (uniformly sampled), $\E[Y]$ has rank $d$, and the entries of $Y$ have bounded support.
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individual's true occupation and a word embedding of their name.
Maximizing Social Influence in Nearly Optimal Time
Finally, we show that this runtime is optimal (up to logarithmic factors) for any beta and fixed seed size k.
Data Structures and Algorithms Social and Information Networks Physics and Society F.2.2; J.4
Strategic Ranking
Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals.
Disincentivizing Polarization in Social Networks
We present a model for content curation and personalization that avoids filter bubbles, along with algorithmic guarantees and nearly matching lower bounds.
Image and Data Mining in Reticular Chemistry Using GPT-4V
The integration of artificial intelligence into scientific research has reached a new pinnacle with GPT-4V, a large language model featuring enhanced vision capabilities, accessible through ChatGPT or an API.
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
We show the efficiency and effectiveness of NeuralMD, with a 2000$\times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.
