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Found 28 papers, 9 papers with code
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification
However, the simple extension of meta-learning approaches to multi-label classification is sub-optimal for LMTC tasks due to long-tailed label distribution and coexisting of few- and zero-shot scenarios.
Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification
Multi-Label Text Classification (MLTC) is a fundamental and challenging task in natural language processing.
Large-scale Foundation Models and Generative AI for BigData Neuroscience
Recent advances in machine learning have made revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery.
CharacterChat: Learning towards Conversational AI with Personalized Social Support
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency.
Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning
Due to shortage of water resources and increasing water demands, the joint operation of multireservoir systems for balancing power generation, ecological protection, and the residential water supply has become a critical issue in hydropower management.
An Information-State Based Approach to Linear Time Varying System Identification and Control
This paper considers the problem of system identification for linear time varying systems.
Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules
Lossless image compression is an essential research field in image compression.
Multi-label Classification with High-rank and High-order Label Correlations
Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization.
Ensemble Clustering via Co-association Matrix Self-enhancement
By propagating the highly-reliable information of the HC matrix to the CA matrix and complementing the HC matrix according to the CA matrix simultaneously, the proposed method generates an enhanced CA matrix for better clustering.
ViT-P: Rethinking Data-efficient Vision Transformers from Locality
We make vision transformers as data-efficient as convolutional neural networks by introducing multi-focal attention bias.
PDWN: Pyramid Deformable Warping Network for Video Interpolation
At the finest scale, the two warped frames are adaptively blended to generate the middle frame.
Adversarial Learning with Cost-Sensitive Classes
This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples.
Incorporating Hidden Layer representation into Adversarial Attacks and Defences
In this paper, we propose a defence strategy to improve adversarial robustness by incorporating hidden layer representation.
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks
This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution.
On the Convergence of Reinforcement Learning in Nonlinear Continuous State Space Problems
We consider the problem of Reinforcement Learning for nonlinear stochastic dynamical systems.
A Review of Generalized Zero-Shot Learning Methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning.
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems.
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension
Machine reading comprehension (MRC) is the task that asks a machine to answer questions based on a given context.
R3: A Reading Comprehension Benchmark Requiring Reasoning Processes
Existing question answering systems can only predict answers without explicit reasoning processes, which hinder their explainability and make us overestimate their ability of understanding and reasoning over natural language.
On the Feedback Law in Stochastic Optimal Nonlinear Control
We consider the problem of nonlinear stochastic optimal control.
On the Search for Feedback in Reinforcement Learning
The problem of Reinforcement Learning (RL) in an unknown nonlinear dynamical system is equivalent to the search for an optimal feedback law utilizing the simulations/ rollouts of the dynamical system.
Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain
Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary classifiers according to a label order.
To Tune or Not To Tune? How About the Best of Both Worlds?
In this regard, Peters et al. perform several experiments which demonstrate that it is better to adapt BERT with a light-weight task-specific head, rather than building a complex one on top of the pre-trained language model, and freeze the parameters in the said language model.
A Survey and Experimental Analysis of Distributed Subgraph Matching
We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.
Databases
Online Distilling from Checkpoints for Neural Machine Translation
Our method on-the-fly generates a teacher model from checkpoints, guiding the training process to obtain better performance.
Decoupled Data Based Approach for Learning to Control Nonlinear Dynamical Systems
This paper proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, `open loop - closed loop', approach.
Realtime Scheduling and Power Allocation Using Deep Neural Networks
With the increasing number of base stations (BSs) and network densification in 5G, interference management using link scheduling and power control are vital for better utilization of radio resources.
Reconstructing Speech Stimuli From Human Auditory Cortex Activity Using a WaveNet Approach
The superior temporal gyrus (STG) region of cortex critically contributes to speech recognition.
