Search Results for author:
Found 16 papers, 4 papers with code
Code as Reward: Empowering Reinforcement Learning with VLMs
Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and provide feedback on task completion.
Transfer Learning for the Prediction of Entity Modifiers in Clinical Text: Application to Opioid Use Disorder Case Detection
Conclusions: We show that learned weights from our shared model can be effectively transferred to a new partially matched data set, validating the use of transfer learning for clinical text modifiers
GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning
In this paper, we evaluate the reasoning capabilities of VLMs along various axes through the lens of geometry problems.
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
This work studies a central extremal graph theory problem inspired by a 1975 conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of nodes) that maximize the number of edges without having 3- or 4-cycles.
AutoMix: Automatically Mixing Language Models
Large language models (LLMs) are now available from cloud API providers in various sizes and configurations.
Policy composition in reinforcement learning via multi-objective policy optimization
In two domains with continuous observation and action spaces, our agents successfully compose teacher policies in sequence and in parallel, and are also able to further extend the policies of the teachers in order to solve the task.
Accelerating exploration and representation learning with offline pre-training
In this work, we follow the hypothesis that exploration and representation learning can be improved by separately learning two different models from a single offline dataset.
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Traditional automated theorem provers for first-order logic depend on speed-optimized search and many handcrafted heuristics that are designed to work best over a wide range of domains.
Training a First-Order Theorem Prover from Synthetic Data
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.
Learning Compositional Structures for Deep Learning: Why Routing-by-agreement is Necessary
We present a formal grammar description of convolutional neural networks and capsule networks that shows how capsule networks can enforce such parse-tree structures, while CNNs do not.
Learning to Prove from Synthetic Theorems
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition
In this work, we first spell out the key requirements for creating a well-curated database of speech samples in non-native accents for training and testing robust ASR systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Block-Value Symmetries in Probabilistic Graphical Models
These orbits are represented compactly using permutations over variables, and variable-value (VV) pairs, but they can miss several state symmetries in a domain.
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
Moreover, algorithms for lifted inference in multi-valued domains also compute a multi-valued extension of count symmetries only.
Coarse-to-Fine Lifted MAP Inference in Computer Vision
We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation.
Contextual Symmetries in Probabilistic Graphical Models
We extend previous work on exploiting symmetries in the MCMC framework to the case of contextual symmetries.
