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Found 37 papers, 10 papers with code
Cross-Lingual Training of Dense Retrievers for Document Retrieval
Dense retrieval has shown great success for passage ranking in English.
Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models
Additionally, prediction targets can vary in the level of detail they encode; targets that encode fine-grained acoustic details are beneficial for denoising tasks, while targets that encode higher-level abstractions are more suited for content-related tasks.
dMel: Speech Tokenization made Simple
Large language models have revolutionized natural language processing by leveraging self-supervised pretraining on vast textual data.
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition
Language models (LMs) have long been used to improve results of automatic speech recognition (ASR) systems, but they are unaware of the errors that ASR systems make.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
How Far Are We from Intelligent Visual Deductive Reasoning?
Vision-Language Models (VLMs) have recently demonstrated incredible strides on diverse vision language tasks.
Divide-or-Conquer? Which Part Should You Distill Your LLM?
Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first.
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling
Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased.
KGLens: Towards Efficient and Effective Knowledge Probing of Large Language Models with Knowledge Graphs
Large Language Models (LLMs) might hallucinate facts, while curated Knowledge Graph (KGs) are typically factually reliable especially with domain-specific knowledge.
Asynchronous Local Computations in Distributed Bayesian Learning
To demonstrate the efficacy of the proposed algorithm, we present simulations on a toy problem as well as on real world data sets to train ML models to perform classification tasks.
Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation
Guided by these observations, we construct a new, improved dataset called LAGRANGE using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation.
Reinforcement Learning-based Control of Nonlinear Systems using Carleman Approximation: Structured and Unstructured Designs
We develop data-driven reinforcement learning (RL) control designs for input-affine nonlinear systems.
Asynchronous Bayesian Learning over a Network
We present a practical asynchronous data fusion model for networked agents to perform distributed Bayesian learning without sharing raw data.
ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech
In this paper, we extend the pretraining method for cross-lingual multi-speaker speech synthesis tasks, including cross-lingual multi-speaker voice cloning and cross-lingual multi-speaker speech editing.
XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing
We also include global translation exemplars for a target language to facilitate the translation process for large language models.
Better Language Model with Hypernym Class Prediction
Class-based language models (LMs) have been long devised to address context sparsity in $n$-gram LMs.
A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing
Recently, speech representation learning has improved many speech-related tasks such as speech recognition, speech classification, and speech-to-text translation.
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions
Achieving distributed reinforcement learning (RL) for large-scale cooperative multi-agent systems (MASs) is challenging because: (i) each agent has access to only limited information; (ii) issues on convergence or computational complexity emerge due to the curse of dimensionality.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph
In this work, we study MARLs with directed coordination graphs, and propose a distributed RL algorithm where the local policy evaluations are based on local value functions.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Cross-Lingual Training with Dense Retrieval for Document Retrieval
Dense retrieval has shown great success in passage ranking in English.
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent
Recently introduced distributed zeroth-order optimization (ZOO) algorithms have shown their utility in distributed reinforcement learning (RL).
Variance Reduction of Quadcopter Trajectory Tracking in Turbulent Wind
In particular, we design a minimum cost variance (MCV) controller aiming to minimize the cost in terms of its weighted sum of mean and variance.
Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid
We consider a UAS that can be fully controlled by the onboard DAA system and by a remote human pilot.
Decentralized Langevin Dynamics for Bayesian Learning
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting.
Online Observer-Based Inverse Reinforcement Learning
In this paper, a novel approach to the output-feedback inverse reinforcement learning (IRL) problem is developed by casting the IRL problem, for linear systems with quadratic cost functions, as a state estimation problem.
Cross-Lingual Training of Neural Models for Document Ranking
We tackle the challenge of cross-lingual training of neural document ranking models for mono-lingual retrieval, specifically leveraging relevance judgments in English to improve search in non-English languages.
Decomposability and Parallel Computation of Multi-Agent LQR
Conditions for decomposability, an algorithm for constructing the transformation matrix, a parallel RL algorithm, and robustness analysis when the design is applied to non-homogeneous MAS are presented.
Hierarchical Reinforcement Learning
Reinforcement Learning (RL)
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation
The first component optimizes the performance of each independent cluster by solving the smaller-size LQR design problem in a model-free way using an RL algorithm.
A Decentralized Approach to Bayesian Learning
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting.
Emulating UAV Motion by Utilizing Robotic Arm for mmWave Wireless Channel Characterization
In this paper, millimeter wave (mmWave) wireless channel characteristics (Doppler spread and path loss modeling) for Unmanned Aerial Vehicles (UAVs) assisted communication is analyzed and studied by emulating the real UAV motion using a robotic arm.
Segatron: Segment-Aware Transformer for Language Modeling and Understanding
To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token.
Ranked #20 on
Language Modelling
on WikiText-103
Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations
We present a set of model-free, reduced-dimensional reinforcement learning (RL) based optimal control designs for linear time-invariant singularly perturbed (SP) systems.
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2
Experimental results show that the Chinese GPT2 can generate better essay endings with \eop.
Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning
Numerical results also show that the proposed distributed algorithm allows the individual agents to recognize the digits even though the training data corresponding to all the digits is not locally available to each agent.
Optimization and Control Systems and Control Systems and Control
Wind Estimation Using Quadcopter Motion: A Machine Learning Approach
In this article, we study the well known problem of wind estimation in atmospheric turbulence using small unmanned aerial systems (sUAS).
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference
Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations.
Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
This paper focuses on the language transferring task given a tiny in-domain parallel SLU corpus.
Cultural Vocal Bursts Intensity Prediction
domain classification
+7
Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
An SLU corpus is a monolingual corpus with domain/intent/slot labels.
Cultural Vocal Bursts Intensity Prediction
domain classification
+9
