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Found 13 papers, 7 papers with code
BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks
Large language models (LLMs) are deep learning algorithms being used to perform natural language processing tasks in various fields, from social sciences to finance and biomedical sciences.
Learning Visual Representation from Human Interactions
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision.
What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision.
Watching the World Go By: Representation Learning from Unlabeled Videos
Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks.
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks.
Shifting the Baseline: Single Modality Performance on Visual Navigation \& QA
We demonstrate the surprising strength of unimodal baselines in multimodal domains, and make concrete recommendations for best practices in future research.
SplitNet: Sim2Sim and Task2Task Transfer for Embodied Visual Navigation
We propose SplitNet, a method for decoupling visual perception and policy learning.
What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning
In this work we propose Hierarchical Planning and Reinforcement Learning (HIP-RL), a method for merging the benefits and capabilities of Symbolic Planning with the learning abilities of Deep Reinforcement Learning.
Shifting the Baseline: Single Modality Performance on Visual Navigation & QA
We demonstrate the surprising strength of unimodal baselines in multimodal domains, and make concrete recommendations for best practices in future research.
AI2-THOR: An Interactive 3D Environment for Visual AI
We introduce The House Of inteRactions (THOR), a framework for visual AI research, available at http://ai2thor. allenai. org.
IQA: Visual Question Answering in Interactive Environments
Our experiments show that our proposed model outperforms popular single controller based methods on IQUAD V1.
Visual Semantic Planning using Deep Successor Representations
A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world.
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time.
