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Found 25 papers, 9 papers with code
Reliable Deep Diffusion Tensor Estimation: Rethinking the Power of Data-Driven Optimization Routine
The former fits DW images from diverse acquisition settings into diffusion tensor field, while the latter applies a deep learning-based denoiser to regularize the diffusion tensor field instead of the DW images, which is free from the limitation of fixed-channel assignment of the network.
Continuous-Time Digital Twin with Analogue Memristive Neural Ordinary Differential Equation Solver
We experimentally validate our approach by developing a digital twin of the HP memristor, which accurately extrapolates its nonlinear dynamics, achieving a 4. 2-fold projected speedup and a 41. 4-fold projected decrease in energy consumption compared to state-of-the-art digital hardware, while maintaining an acceptable error margin.
Edge Intelligence Optimization for Large Language Model Inference with Batching and Quantization
Specifically, with the deployment of the batching technique and model quantization on resource-limited edge devices, we formulate an inference model for transformer decoder-based LLMs.
FastFace: Fast-converging Scheduler for Large-scale Face Recognition Training with One GPU
Computing power has evolved into a foundational and indispensable resource in the area of deep learning, particularly in tasks such as Face Recognition (FR) model training on large-scale datasets, where multiple GPUs are often a necessity.
Efficient and accurate neural field reconstruction using resistive memory
The GE harnesses the intrinsic stochasticity of resistive memory for efficient input encoding, while the PE achieves precise weight mapping through a Hardware-Aware Quantization (HAQ) circuit.
Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
Demonstrating equivalent generative quality to the software baseline, our system achieved remarkable enhancements in generative speed for both unconditional and conditional generation tasks, by factors of 64. 8 and 156. 5, respectively.
X2-Softmax: Margin Adaptive Loss Function for Face Recognition
Learning the discriminative features of different faces is an important task in face recognition.
Fast and Accurate Factual Inconsistency Detection Over Long Documents
We introduce SCALE (Source Chunking Approach for Large-scale inconsistency Evaluation), a task-agnostic model for detecting factual inconsistencies using a novel chunking strategy.
When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs
Interestingly, we find that the transformer PLMs tend to score GPT-generated text 10-15\% higher on average, relative to human-authored documents.
Cognitive Semantic Communication Systems Driven by Knowledge Graph
Semantic communication is envisioned as a promising technique to break through the Shannon limit.
GIU-GANs: Global Information Utilization for Generative Adversarial Networks
To overcome the aforementioned limitations, in this paper, we propose a new GANs called Involution Generative Adversarial Networks (GIU-GANs).
KDCTime: Knowledge Distillation with Calibration on InceptionTime for Time-series Classification
Time-series classification approaches based on deep neural networks are easy to be overfitting on UCR datasets, which is caused by the few-shot problem of those datasets.
Deep-Learning-Enabled Inverse Engineering of Multi-Wavelength Invisibility-to-Superscattering Switching with Phase-Change Materials
Inverse design of nanoparticles for desired scattering spectra and dynamic switching between the two opposite scattering anomalies, i. e. superscattering and invisibility, is important in realizing cloaking, sensing and functional devices.
Optics
Semantic Matching for Sequence-to-Sequence Learning
In sequence-to-sequence models, classical optimal transport (OT) can be applied to semantically match generated sentences with target sentences.
Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes
High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task.
Dynamic Embedding on Textual Networks via a Gaussian Process
The relative importance of global versus local structure for the embeddings is learned automatically.
Improving Textual Network Learning with Variational Homophilic Embeddings
This paper considers a novel variational formulation of network embeddings, with special focus on textual networks.
Syntax-Infused Variational Autoencoder for Text Generation
We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences.
Improving Textual Network Embedding with Global Attention via Optimal Transport
Constituting highly informative network embeddings is an important tool for network analysis.
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment
Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years.
Diffusion Maps for Textual Network Embedding
Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices.
Joint Embedding of Words and Labels for Text Classification
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences.
Ranked #11 on
Text Classification
on DBpedia
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Low-rank signal modeling has been widely leveraged to capture non-local correlation in image processing applications.
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector.
