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Found 15 papers, 8 papers with code
Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection
Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features.
Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent
Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate rationales.
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
Therefore, in this paper, based on audio and text, we consider the task of multimodal sentiment analysis and propose a novel fusion strategy including both multi-feature fusion and multi-modality fusion to improve the accuracy of audio-text sentiment analysis.
Multimodal Emotion Recognition
Multimodal Sentiment Analysis
Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network
Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods.
PG$^2$Net: Personalized and Group Preferences Guided Network for Next Place Prediction
Targeting this, we propose an end-to-end framework named personalized and group preference guided network (PG$^2$Net), considering the users' preferences to various places at both individual and collective levels.
Enhancing Unsupervised Anomaly Detection with Score-Guided Network
Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems.
UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment
Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science.
Trainable back-propagated functional transfer matrices
In experiments, it is demonstrated that the revised rules can be used to train a range of functional connections: 20 different functions are applied to neural networks with up to 10 hidden layers, and most of them gain high test accuracies on the MNIST database.
Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural Networks
Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans.
Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach
Travel time estimation is a crucial task for not only personal travel scheduling but also city planning.
Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU
With all these modifications, the size of the PHASEN model is shrunk from 33M parameters to 5M parameters, while the performance on VoiceBank+DEMAND is improved to the CSIG score of 4. 30, the PESQ score of 3. 07 and the COVL score of 3. 73.
An Empirical Study on End-to-End Singing Voice Synthesis with Encoder-Decoder Architectures
With the rapid development of neural network architectures and speech processing models, singing voice synthesis with neural networks is becoming the cutting-edge technique of digital music production.
Explaining the Attention Mechanism of End-to-End Speech Recognition Using Decision Trees
The attention mechanism has largely improved the performance of end-to-end speech recognition systems.
SanMove: Next Location Recommendation via Self-Attention Network
To address the above challenges, we propose a novel method named SanMove, a self-attention network based model, to predict the next location via capturing the long- and short-term mobility patterns of users.
Swap-based Deep Reinforcement Learning for Facility Location Problems in Networks
Facility location problems on graphs are ubiquitous in real world and hold significant importance, yet their resolution is often impeded by NP-hardness.
