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Found 10 papers, 0 papers with code
Task-Balanced Distillation for Object Detection
To alleviate this problem, a novel Task-decoupled Feature Distillation (TFD) is proposed by flexibly balancing the contributions of classification and regression tasks.
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention
Safety has become one of the main challenges of applying deep reinforcement learning to real world systems.
Multi-Faceted Representation Learning with Hybrid Architecture for Time Series Classification
Time series classification problems exist in many fields and have been explored for a couple of decades.
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback
Forward channel state information (CSI) often plays a vital role in scheduling and capacity-approaching transmission optimization for massive multiple-input multiple-output (MIMO) communication systems.
Text Classification based on Multi-granularity Attention Hybrid Neural Network
Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural network (ConvNet).
Multichannel CNN with Attention for Text Classification
Alternatively, CNN is able to capture n-gram features of texts by utilizing convolutional filters.
Human Activity Recognition based on Dynamic Spatio-Temporal Relations
In the proposed method, an activity is divided into several successive actions represented by spatio temporal patterns, and the evolution of these actions are captured by a sequential model.
A Novel Decision Tree for Depression Recognition in Speech
Depression is a common mental disorder worldwide which causes a range of serious outcomes.
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design
The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution neural network on embedded platforms.
