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Found 38 papers, 16 papers with code
Selective Explanations: Leveraging Human Input to Align Explainable AI
As a result, current XAI techniques are often found to be hard to use and lack effectiveness.
Active Example Selection for In-Context Learning
For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a $5. 8\%$ improvement on average.
Aircraft Ground Taxiing Deduction and Conflict Early Warning Method Based on Control Command Information
Aircraft taxiing conflict is a threat to the safety of airport operations, mainly due to the human error in control command infor-mation.
Data-Augmented Counterfactual Learning for Bundle Recommendation
In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve bundle recommendation.
CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization
The imitation learning of self-driving vehicle policies through behavioral cloning is often carried out in an open-loop fashion, ignoring the effect of actions to future states.
Learning to Ignore Adversarial Attacks
Despite the strong performance of current NLP models, they can be brittle against adversarial attacks.
Caption Feature Space Regularization for Audio Captioning
To eliminate this negative effect, in this paper, we propose a two-stage framework for audio captioning: (i) in the first stage, via the contrastive learning, we construct a proxy feature space to reduce the distances between captions correlated to the same audio, and (ii) in the second stage, the proxy feature space is utilized as additional supervision to encourage the model to be optimized in the direction that benefits all the correlated captions.
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis
The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval 2014 Task 4 Sub Task 2
(using extra training data)
Aspect-Based Sentiment Analysis (ABSA)
Multi-Task Learning
+1
Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation
Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation.
Temporal aware Multi-Interest Graph Neural Network For Session-based Recommendation
Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences.
Automatic Configuration for Optimal Communication Scheduling in DNN Training
ByteScheduler partitions and rearranges tensor transmissions to improve the communication efficiency of distributed Deep Neural Network (DNN) training.
Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation
As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
In this paper, we propose a holistic framework named MetaHG to automatically detect illicit drug traffickers on social media (i. e., Instagram), by tackling the following two new challenges: (1) different from existing works which merely focus on analyzing post content, MetaHG is capable of jointly modeling multi-modal content and relational structured information on social media for illicit drug trafficker detection; (2) in addition, through the proposed meta-learning technique, MetaHG addresses the issue of requiring sufficient data for model training.
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
In particular, Truncated Quantile Critics (TQC) achieves state-of-the-art asymptotic training performance on the MuJoCo benchmark with a distributional representation of critics; and Randomized Ensemble Double Q-Learning (REDQ) achieves high sample efficiency that is competitive with state-of-the-art model-based methods using a high update-to-data ratio and target randomization.
EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks
EmbRace introduces Sparsity-aware Hybrid Communication, which integrates AlltoAll and model parallelism into data-parallel training, so as to reduce the communication overhead of highly sparse parameters.
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation
In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation.
Multi-behavior Graph Contextual Aware Network for Session-based Recommendation
Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii).
Nonholonomic dynamics and control of road vehicles: moving toward automation
The models are categorized based on how they represent the wheel-ground contact, whether they incorporate the longitudinal dynamics, and whether they consider the steering dynamics.
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
Based on this bound, we develop an iterative procedure which produces a sequence of monotonically improved policies for the average reward criterion.
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning
Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks.
Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models
Remarkably, we find that the physics learned from the first specimen allows us to understand the backscattering observed in the latter sample, a qualitative feature that is wholly absent from the specimen from which the physics were inferred.
Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations
Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all.
MAX Phase Zr2SeC and Its Thermal Conduction Behavior
The elemental diversity is crucial to screen out ternary MAX phases with outstanding properties via tuning of bonding types and strength between constitutive atoms.
Materials Science
Average Reward Reinforcement Learning with Monotonic Policy Improvement
In continuing control tasks, an agent’s average reward per time step is a more natural performance measure compared to the commonly used discounting framework as it can better capture an agent’s long-term behavior.
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems
The region of interest can be specified based on the localization features of the solution, user interest, and correlation length of the random material properties.
AinnoSeg: Panoramic Segmentation with High Perfomance
Panoramic segmentation is a scene where image segmentation tasks is more difficult.
MLCVNet: Multi-Level Context VoteNet for 3D Object Detection
We demonstrate these by capturing contextual information at patch, object and scene levels.
Advances in Bayesian Probabilistic Modeling for Industrial Applications
The methodology, called GE's Bayesian Hybrid Modeling (GEBHM), is a probabilistic modeling method, based on the Kennedy and O'Hagan framework, that has been continuously scaled-up and industrialized over several years.
First Order Constrained Optimization in Policy Space
We propose a novel approach called First Order Constrained Optimization in Policy Space (FOCOPS) which maximizes an agent's overall reward while ensuring the agent satisfies a set of cost constraints.
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension
In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty
Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification.
SUPERVISED POLICY UPDATE
We show how the Natural Policy Gradient and Trust Region Policy Optimization (NPG/TRPO) problems, and the Proximal Policy Optimization (PPO) problem can be addressed by this methodology.
Automatic Opioid User Detection From Twitter: Transductive Ensemble Built On Different Meta-graph Based Similarities Over Heterogeneous Information Network
Opioid (e. g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States.
Efficient Entropy for Policy Gradient with Multidimensional Action Space
We develop several novel unbiased estimators for the entropy bonus and its gradient.
Supervised Policy Update for Deep Reinforcement Learning
We show how the Natural Policy Gradient and Trust Region Policy Optimization (NPG/TRPO) problems, and the Proximal Policy Optimization (PPO) problem can be addressed by this methodology.
Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions
Depthwise convolutions provide significant performance benefits owing to the reduction in both parameters and mult-adds.
Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus
In the case of high-dimensional action spaces, calculating the entropy and the gradient of the entropy requires enumerating all the actions in the action space and running forward and backpropagation for each action, which may be computationally infeasible.
