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Found 38 papers, 15 papers with code
Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach
Large language models (LLMs) are highly capable of many tasks but they can sometimes generate unreliable or inaccurate outputs.
PromptCodec: High-Fidelity Neural Speech Codec using Disentangled Representation Learning based Adaptive Feature-aware Prompt Encoders
Neural speech codec has recently gained widespread attention in generative speech modeling domains, like voice conversion, text-to-speech synthesis, etc.
Cumulative Distribution Function based General Temporal Point Processes
Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.
Preparing Lessons for Progressive Training on Language Models
The rapid progress of Transformers in artificial intelligence has come at the cost of increased resource consumption and greenhouse gas emissions due to growing model sizes.
Transforming Agriculture with Intelligent Data Management and Insights
At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the \textit{de facto} data management and analysis practices adopted by scientists have become increasingly inefficient.
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks
Federated learning (FL) is a widely employed distributed paradigm for collaboratively training machine learning models from multiple clients without sharing local data.
Enhanced Federated Optimization: Adaptive Unbiased Sampling with Reduced Variance
Central to this process is the technique of unbiased client sampling, which ensures a representative selection of clients.
Tackling Hybrid Heterogeneity on Federated Optimization via Gradient Diversity Maximization
The combined effects of statistical and system heterogeneity can significantly reduce the efficiency of federated optimization.
PromptVC: Flexible Stylistic Voice Conversion in Latent Space Driven by Natural Language Prompts
In this study, we propose PromptVC, a novel style voice conversion approach that employs a latent diffusion model to generate a style vector driven by natural language prompts.
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition
Despite notable progress, speech emotion recognition (SER) remains challenging due to the intricate and ambiguous nature of speech emotion, particularly in wild world.
Quantity-Aware Coarse-to-Fine Correspondence for Image-to-Point Cloud Registration
Image-to-point cloud registration aims to determine the relative camera pose between an RGB image and a reference point cloud, serving as a general solution for locating 3D objects from 2D observations.
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition
Contrastive cross-modality pretraining has recently exhibited impressive success in diverse fields, whereas there is limited research on their merits in speech emotion recognition (SER).
Tensorized Hypergraph Neural Networks
THNN is a faithful hypergraph modeling framework through high-order outer product feature message passing and is a natural tensor extension of the adjacency-matrix-based graph neural networks.
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism
SqueezeFormer has recently shown impressive performance in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Single-photon Image Super-resolution via Self-supervised Learning
Single-Photon Image Super-Resolution (SPISR) aims to recover a high-resolution volumetric photon counting cube from a noisy low-resolution one by computational imaging algorithms.
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
Interestingly, although these two types of networks originate from different observations, they are inherently linked through the common multilinearity structure underlying both TNs and NNs, thereby motivating a significant number of intellectual developments regarding combinations of TNs and NNs.
LMEC: Learnable Multiplicative Absolute Position Embedding Based Conformer for Speech Recognition
This paper proposes a Learnable Multiplicative absolute position Embedding based Conformer (LMEC).
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs.
Multiple Domain Cyberspace Attack and Defense Game Based on Reward Randomization Reinforcement Learning
In order to improve the defense ability of defender, a game model based on reward randomization reinforcement learning is proposed.
KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning
Following that, we proposed a user's permissions reasoning method based on reinforcement learning.
Semantically Proportional Patchmix for Few-Shot Learning
Few-shot learning aims to classify unseen classes with only a limited number of labeled data.
Deep Domain Adversarial Adaptation for Photon-efficient Imaging
Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel.
Robust photon-efficient imaging using a pixel-wise residual shrinkage network
Single-photon light detection and ranging (LiDAR) has been widely applied to 3D imaging in challenging scenarios.
Assessing Deep Neural Networks as Probability Estimators
Deep Neural Networks (DNNs) have performed admirably in classification tasks.
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification.
Fast query-by-example speech search using separable model
Second, we design a novel model inference scheme based on RepVGG which can efficiently improve the QbE search quality.
Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlations
We prove that such a rule is much more relaxed than that of TT, which means ResTT can easily address the vanishing and exploding gradient problem that exists in the existing TT models.
AFINet: Attentive Feature Integration Networks for Image Classification
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification.
TedNet: A Pytorch Toolkit for Tensor Decomposition Networks
Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures.
Detecting quantum entanglement with unsupervised learning
Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks.
Spectrum Attention Mechanism for Time Series Classification
Since time series always contains a lot of noise, which has a negative impact on network training, people usually filter the original data before training the network.
RegNet: Self-Regulated Network for Image Classification
The ResNet and its variants have achieved remarkable successes in various computer vision tasks.
Ranked #3 on
Medical Image Classification
on NCT-CRC-HE-100K
AFINets: Attentive Feature Integration Networks for Image Classification
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks, e. g., image classification.
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region.
Quantum Language Model with Entanglement Embedding for Question Answering
Quantum Language Models (QLMs) in which words are modelled as quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability.
Making Adversarial Examples More Transferable and Indistinguishable
Fast gradient sign attack series are popular methods that are used to generate adversarial examples.
nuScenes: A multimodal dataset for autonomous driving
Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.
Ranked #312 on
3D Object Detection
on nuScenes
(using extra training data)
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling.
