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Found 21 papers, 8 papers with code
Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation
To address these challenges, first, we evaluate a multi-target and a multi-dataset training settings by training one model on each dataset and datasets of different domains, respectively.
A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution
Based on Pre-trained Language Models (PLMs), event coreference resolution (ECR) systems have demonstrated outstanding performance in clustering coreferential events across documents.
CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models
Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language.
BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation
Boolean algebraic manipulation is at the core of logic synthesis in Electronic Design Automation (EDA) design flow.
Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designs
The complexity of modern hardware designs necessitates advanced methodologies for optimizing and analyzing modern digital systems.
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners
Active learning (AL), which aims to construct an effective training set by iteratively curating the most formative unlabeled data for annotation, has been widely used in low-resource tasks.
Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information
However, we observe that merely concatenating sentences in a contextual window does not fully utilize contextual information and can sometimes lead to excessive attention on less informative sentences.
Improving Rating Prediction in Multi-Criteria Recommender Systems via a Collective Factor Model
Experiments on three real-world datasets show that our proposed architecture achieves up to 13. 14% lower prediction error over baseline approaches.
Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture
Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed.
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs
Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e. g., computation, I/O, and memory-bound) edge computing systems.
Physics-aware Roughness Optimization for Diffractive Optical Neural Networks
As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural networks due to extreme fast computation speed (light speed) and low energy consumption.
Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks
Diffractive optical neural networks (DONNs) have attracted lots of attention as they bring significant advantages in terms of power efficiency, parallelism, and computational speed compared with conventional deep neural networks (DNNs), which have intrinsic limitations when implemented on digital platforms.
QuickSkill: Novice Skill Estimation in Online Multiplayer Games
However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player.
Field-aware Variational Autoencoders for Billion-scale User Representation Learning
Moreover, we deploy the proposed method in real-world applications and conduct online A/B tests in a look-alike system.
Differentiable Discrete Device-to-System Codesign for Optical Neural Networks via Gumbel-Softmax
Specifically, Gumbel-Softmax with a novel complex-domain regularization method is employed to enable differentiable one-to-one mapping from discrete device parameters into the forward function of DONNs, where the physical parameters in DONNs can be trained by simply minimizing the loss function of the ML task.
Stance Detection in COVID-19 Tweets
The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e. g. stay at home mandates and wearing face masks when out in public.
Target-Aware Data Augmentation for Stance Detection
The goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target.
Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design
Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments.
Multi-Task Stance Detection with Sentiment and Stance Lexicons
Stance detection aims to detect whether the opinion holder is in support of or against a given target.
