Search Results for author:
Found 11 papers, 2 papers with code
PRIMA: Multi-Image Vision-Language Models for Reasoning Segmentation
Despite significant advancements in Large Vision-Language Models (LVLMs), existing pixel-grounding models operate on single-image settings, limiting their ability to perform detailed, fine-grained comparisons across multiple images.
General Causal Imputation via Synthetic Interventions
The task of causal imputation involves using this subset to predict unobserved interactions.
Masked Generative Priors Improve World Models Sequence Modelling Capabilities
Building on the Efficient Stochastic Transformer-based World Models (STORM) architecture, we replace the traditional MLP prior with a Masked Generative Prior (e. g., MaskGIT Prior) and introduce GIT-STORM.
AI-Assisted Generation of Difficult Math Questions
We present a design framework that combines the strengths of LLMs with a human-in-the-loop approach to generate a diverse array of challenging math questions.
Towards DNA-Encoded Library Generation with GFlowNets
DNA-encoded libraries (DELs) are a powerful approach for rapidly screening large numbers of diverse compounds.
Efficient Causal Graph Discovery Using Large Language Models
We propose a novel framework that leverages LLMs for full causal graph discovery.
Unlearning via Sparse Representations
Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible by existing techniques.
Neural Feature-Adaptation for Symbolic Predictions Using Pre-Training and Semantic Loss
Real data is often imperfect meaning that even if the symbolic theory remains unchanged, we may still need to address the problem of mapping raw data to high-level symbols, each time there is a change in the data acquisition environment or equipment.
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning
We formalize the notions of coordination level and heterogeneity level of an environment and present HECOGrid, a suite of multi-agent RL environments that facilitates empirical evaluation of different MARL approaches across different levels of coordination and environmental heterogeneity by providing a quantitative control over coordination and heterogeneity levels of the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel
In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another.
Intelligent Communication
Multi-agent Reinforcement Learning
+2
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!
We benchmark the baseline techniques on this synthetic data as well as use it for data augmentation.
