Search Results for author:
Found 28 papers, 4 papers with code
Wolf: Captioning Everything with a World Summarization Framework
Finally, we establish a benchmark for video captioning and introduce a leaderboard, aiming to accelerate advancements in video understanding, captioning, and data alignment.
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving
The autonomous driving industry is increasingly adopting end-to-end learning from sensory inputs to minimize human biases in system design.
Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning
The increasing complexity of tasks in robotics demands efficient strategies for multitask and continual learning.
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention
Instead of inferring the complete human behavior characteristics, MEReQ infers a residual reward function that captures the discrepancy between the human expert's and the prior policy's underlying reward functions.
What Matters to You? Towards Visual Representation Alignment for Robot Learning
When operating in service of people, robots need to optimize rewards aligned with end-user preferences.
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization
We observe that, generally, a more diverse set of co-play agents during training enhances the generalization performance of the ego agent; however, this improvement varies across distinct scenarios and environments.
Towards Modeling and Influencing the Dynamics of Human Learning
Interestingly, robot actions influence what this experience is, and therefore influence how people's internal models change.
Amos: An Adam-style Optimizer with Adaptive Weight Decay towards Model-Oriented Scale
We present Amos, a stochastic gradient-based optimizer designed for training deep neural networks.
Simple Recurrence Improves Masked Language Models
In this work, we explore whether modeling recurrence into the Transformer architecture can both be beneficial and efficient, by building an extremely simple recurrent module into the Transformer.
Shatter: An Efficient Transformer Encoder with Single-Headed Self-Attention and Relative Sequence Partitioning
The highly popular Transformer architecture, based on self-attention, is the foundation of large pretrained models such as BERT, that have become an enduring paradigm in NLP.
Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data
In this work, we advocate that humans are bounded rational and have different intelligence levels when reasoning about others' decision-making process, and such an inherent and latent characteristic should be accounted for in reward learning algorithms.
Local Additivity Based Data Augmentation for Semi-supervised NER
Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data.
Bounded Risk-Sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory
Classical game-theoretic approaches for multi-agent systems in both the forward policy design problem and the inverse reward learning problem often make strong rationality assumptions: agents perfectly maximize expected utilities under uncertainties.
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation
We address the issue of hallucination in data-to-text generation, i. e., reducing the generation of text that is unsupported by the source.
Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles.
Robotics Systems and Control Systems and Control
Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning
It is a long-standing goal of artificial intelligence (AI) to be superior to human beings in decision making.
Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts
In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection.
Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings
Word embeddings are known to boost performance of many NLP tasks such as text classification, meanwhile they can be enhanced by labels at the document level to capture nuanced meaning such as sentiment and topic.
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder
Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base.
The Challenge of Composition in Distributional and Formal Semantics
Only a limited part of the contents in this tutorial is drawn from the previous one.
Learning Semantically and Additively Compositional Distributional Representations
This paper connects a vector-based composition model to a formal semantics, the Dependency-based Compositional Semantics (DCS).
Question-Answering with Logic Specific to Video Games
We chose a popular game called {`}Minecraft{'}, and created a QA corpus with a knowledge database related to this game and the ontology of a meaning representation that will be used to structure this database.
The Mechanism of Additive Composition
Additive composition (Foltz et al, 1998; Landauer and Dumais, 1997; Mitchell and Lapata, 2010) is a widely used method for computing meanings of phrases, which takes the average of vector representations of the constituent words.
