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Found 30 papers, 10 papers with code
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm
To improve the contextual learning capabilities of LLMs in text-to-SQL, a workflow paradigm method is proposed, aiming to enhance the attention and problem-solving scope of LLMs through decomposition.
Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
This problem is even more severe in cell-free networks as there are many of these parameters to be acquired.
Striking The Right Balance: Three-Dimensional Ocean Sound Speed Field Reconstruction Using Tensor Neural Networks
Accurately reconstructing a three-dimensional ocean sound speed field (3D SSF) is essential for various ocean acoustic applications, but the sparsity and uncertainty of sound speed samples across a vast ocean region make it a challenging task.
Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing
The performance of our proposed method is compared with benchmark methods, including compressive sensing (CS), BCS, and Laplacian-prior BCS (L-BCS).
Overcoming Beam Squint in Dual-Wideband mmWave MIMO Channel Estimation: A Bayesian Multi-Band Sparsity Approach
A probabilistic model is built to induce the common sparsity in the spatial domain, and the first-order Taylor expansion is adopted to get rid of the grid mismatch in the dictionaries.
To Fold or Not to Fold: Graph Regularized Tensor Train for Visual Data Completion
Tensor train (TT) representation has achieved tremendous success in visual data completion tasks, especially when it is combined with tensor folding.
Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?
In this paper, we explain the inference logic of large language models (LLMs) as a set of symbolic concepts.
GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning
GDOD decomposes gradients into task-shared and task-conflict components explicitly and adopts a general update rule for avoiding interference across all task gradients.
Output-Dependent Gaussian Process State-Space Model
Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade.
Tele-Knowledge Pre-training for Fault Analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths
Sequential data naturally have different lengths in many domains, with some very long sequences.
Ranked #4 on
Long-range modeling
on LRA
Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning
Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences.
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling
However, a come back of Bayesian methods is taking place that sheds new light on the design of deep neural networks, which also establish firm links with Bayesian models and inspire new paths for unsupervised learning, such as Bayesian tensor decomposition.
Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention
The overall computing cost of the new building block is as low as $O(N \log N)$.
Ranked #18 on
Long-range modeling
on LRA
Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback
The downlink channel covariance matrix (CCM) acquisition is the key step for the practical performance of massive multiple-input and multiple-output (MIMO) systems, including beamforming, channel tracking, and user scheduling.
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference
Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved the performance of multidisciplinary machine learning tasks such as recommendation systems, genotype imputation and image inpainting.
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search
To further adapt to both various data distributions and different types of devices with heterogeneous embedded hardware platforms, inspired by meta-learning, a Cluster Federated Direct Neural Architecture Search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense that each device can learn a tailored deep learning model for its particular data distribution and hardware constraint.
Tensor-based Basis Function Learning for Three-dimensional Sound Speed Fields
In particular, a tensor-based basis function learning framework is proposed, which can include the classical basis functions (using EOFs and/or Fourier basis functions) as its special cases.
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks
Recurrent Neural Networks, Transformers, and Convolutional Neural Networks are three major techniques for learning from sequential data.
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved.
Sparse Factorization of Large Square Matrices
The sparse factorization method is tested for a variety of synthetic and real-world square matrices.
Ranked #17 on
Long-range modeling
on LRA
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain.
Similarity Embedding Networks for Robust Human Activity Recognition
Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently.
Towards Overfitting Avoidance: Tuning-free Tensor-aided Multi-user Channel Estimation for 3D Massive MIMO Communications
Channel estimation has long been deemed as one of the most critical problems in three-dimensional (3D) massive multiple-input multiple-output (MIMO), which is recognized as the leading technology that enables 3D spatial signal processing in the fifth-generation (5G) wireless communications and beyond.
FDNAS: Improving Data Privacy and Model Diversity in AutoML
To further adapt for various data distributions of clients, inspired by meta-learning, a cluster Federated Direct Neural Architecture Search (CFDNAS) framework is proposed to achieve client-aware NAS, in the sense that each client can learn a tailored deep learning model for its particular data distribution.
Pushing The Limit of Type I Codebook For FDD Massive MIMO Beamforming: A Channel Covariance Reconstruction Approach
There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems.
Tensor Train Factorization and Completion under Noisy Data with Prior Analysis and Rank Estimation
Tensor train (TT) decomposition, a powerful tool for analyzing multidimensional data, exhibits superior performance in many machine learning tasks.
Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning
However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices' energy consumption, and the number of samples for different jobs?
Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior
However, the optimal determination of a tensor rank is known to be a non-deterministic polynomial-time hard (NP-hard) task.
Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders
A crucial challenge in image-based modeling of biomedical data is to identify trends and features that separate normality and pathology.
