Search Results for author:
Found 21 papers, 3 papers with code
Seq2Path: Generating Sentiment Tuples as Paths of a Tree
A tree can represent “1-to-n” relations (e. g., an aspect term may correspond to multiple opinion terms) and the paths of a tree are independent and do not have orders.
Ranked #2 on
Aspect-Based Sentiment Analysis (ABSA)
on ASTE
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Grid-Mapping Pseudo-Count Constraint for Offline Reinforcement Learning
Offline reinforcement learning learns from a static dataset without interacting with the environment, which ensures security and thus owns a good prospect of application.
Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction
Emotion-Cause Pair Extraction (ECPE) involves extracting clause pairs representing emotions and their causes in a document.
Partially Law-Invariant Risk Measures
A notion of strong partial law invariance is introduced, allowing for a representation formula akin to the classical one.
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
To overcome these limitations, we propose a novel DRO approach that employs the Wasserstein distance instead.
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health
In this paper, we consider a risk-averse multi-armed bandit (MAB) problem where the goal is to learn a policy that minimizes the risk of low expected return, as opposed to maximizing the expected return itself, which is the objective in the usual approach to risk-neutral MAB.
A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games
Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and rely on bandit feedback in the form of the cost values of the selected actions at every episode to estimate their CVaR values and update their actions.
Constrained Sequence-to-Tree Generation for Hierarchical Text Classification
Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy.
Risk-Averse No-Regret Learning in Online Convex Games
To address this challenge, we propose a new online risk-averse learning algorithm that relies on one-point zeroth-order estimation of the CVaR gradients computed using CVaR values that are estimated by appropriately sampling the cost functions.
Hybrid Curriculum Learning for Emotion Recognition in Conversation
Emotion recognition in conversation (ERC) aims to detect the emotion label for each utterance.
Ranked #8 on
Emotion Recognition in Conversation
on DailyDialog
Visual Inference and Graphical Representation in Regression Discontinuity Designs
Despite the widespread use of graphs in empirical research, little is known about readers' ability to process the statistical information they are meant to convey ("visual inference").
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
We assume that this context is not accessible to a learner agent who can only observe the expert data.
Packet-Level Adversarial Network Traffic Crafting using Sequence Generative Adversarial Networks
To bypass the detection of NIDS, the generated network traffic and benign traffic are classified by a black-box NIDS.
Decision Making
Image Generation
+1
Cryptography and Security
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis
Some recent work focused on solving a combination of two subtasks, e. g., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely.
Aspect-Based Sentiment Analysis
Aspect-oriented Opinion Extraction
+6
Deformable and robust core-shell protein microcapsules templated by liquid-liquid phase separated microdroplets
Microfluidic mechanical testing revealed that the mechanical robustness of thicker-shell capsules could be controlled through modulation of the shell thickness.
Soft Condensed Matter Biological Physics
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
TNT: Target-driveN Trajectory Prediction
Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states.
On Loss Functions and Recurrency Training for GAN-based Speech Enhancement Systems
Recent work has shown that it is feasible to use generative adversarial networks (GANs) for speech enhancement, however, these approaches have not been compared to state-of-the-art (SOTA) non GAN-based approaches.
Audio and Speech Processing Sound
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e. g. pedestrians and vehicles) and road context information (e. g. lanes, traffic lights).
Predicting the Popularity of Online Videos via Deep Neural Networks
Predicting the popularity of online videos is important for video streaming content providers.
Image Inpainting Using Directional Tensor Product Complex Tight Framelets
In this paper, we establish the grouping effect property for frame-based convex minimization models using the balanced approach.
