activity
20162025
most citedEdward: A library for probabilistic modeling, inference, and criticism

223 citations · 556 across the 23 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

cs.CV2024★ 2 cited

Beyond Aesthetics: Cultural Competence in Text-to-Image Models

Nithish Kannen, Arif Ahmad, Marco Andreetto +5

Text-to-Image (T2I) models are being increasingly adopted in diverse global communities where they create visual representations of their unique cultures. Current T2I benchmarks pr…

cs.LG2024★ 1 cited

Constraint-Aware Diffusion Models for Trajectory Optimization

Anjian Li, Zihan Ding, Adji Bousso Dieng +1

The diffusion model has shown success in generating high-quality and diverse solutions to trajectory optimization problems. However, diffusion models with neural networks inevitabl…

cs.LG2024

Behavior-Inspired Neural Networks for Relational Inference

Yulong Yang, Bowen Feng, Keqin Wang +3

From pedestrians to Kuramoto oscillators, interactions between agents govern how dynamical systems evolve in space and time. Discovering how these agents relate to each other has t…

stat.ML2024

Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design

Quan Nguyen, Adji Bousso Dieng

Experimental design techniques such as active search and Bayesian optimization are widely used in the natural sciences for data collection and discovery. However, existing techniqu…

stat.ML2024

Alternators For Sequence Modeling

Mohammad Reza Rezaei, Adji Bousso Dieng

This paper introduces alternators, a novel family of non-Markovian dynamical models for sequences. An alternator features two neural networks: the observation trajectory network (O…

cs.RO2024★ 3 cited

DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization

Anjian Li, Zihan Ding, Adji Bousso Dieng +1

Optimal trajectory design is computationally expensive for nonlinear and high-dimensional dynamical systems. The challenge arises from the non-convex nature of the optimization pro…