Search Results for author:
Found 18 papers, 8 papers with code
Real-time Sampling and Estimation on Random Access Channels: Age of Information and Beyond
It is shown that non-oblivious policies offer a multiplicative gain close to $3$ compared to oblivious policies.
Timely Broadcasting in Erasure Networks: Age-Rate Tradeoffs
The interplay between timeliness and rate efficiency is investigated in packet erasure broadcast channels with feedback.
Information Theory Social and Information Networks Systems and Control Systems and Control Information Theory
Enhancing Natural Language Representation with Large-Scale Out-of-Domain Commonsense
In this paper, we propose to use large-scale out-of-domain commonsense to enhance text representation.
Open Rule Induction
One weakness of the previous rule induction systems is that they only find rules within a knowledge base (KB) and therefore cannot generalize to more open and complex real-world rules.
1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector
In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection -- Subtask 2 of Shared task 3~\cite{tan-etal-2022-event} at CASE 2022.
1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data
This paper details our participation in the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) workshop @ EMNLP 2022, where we take part in Subtask 1 of Shared Task 3.
Instance-based Learning for Knowledge Base Completion
In this paper, we propose a new method for knowledge base completion (KBC): instance-based learning (IBL).
MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning
The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL).
Containing a spread through sequential learning: to exploit or to explore?
In this work, through an active learning approach, we design testing and isolation strategies to contain the spread and minimize the cumulative infections under a given test budget.
MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif Editing
Is there a unified framework for graph-based retrosynthesis prediction?
Exploring Automatically Perturbed Natural Language Explanations in Relation Extraction
Previous research has demonstrated that natural language explanations provide valuable inductive biases that guide models, thereby improving the generalization ability and data efficiency.
Free Lunch for Efficient Textual Commonsense Integration in Language Models
One key observation is that the upper bound of batch partitioning can be reduced to the classic {\it graph k-cut problem}.
TPDM: Selectively Removing Positional Information for Zero-shot Translation via Token-Level Position Disentangle Module
Due to Multilingual Neural Machine Translation's (MNMT) capability of zero-shot translation, many works have been carried out to fully exploit the potential of MNMT in zero-shot translation.
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored.
MARBLE: Music Audio Representation Benchmark for Universal Evaluation
This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark.
On the Effectiveness of Speech Self-supervised Learning for Music
Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech.
Age of Computing: A Metric of Computation Freshness in Communication and Computation Cooperative Networks
In this letter, we introduce the novel concept, Age of Computing (AoC), to capture computation freshness in 3CNs.
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies, considering both oblivious (where decision-making is independent of the physical processes) and non-oblivious policies (where decision-making depends on physical processes).
