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Found 33 papers, 8 papers with code
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge
Large language models (LLMs) have been widely studied for their ability to store and utilize positive knowledge.
Efficient and Secure Federated Learning for Financial Applications
Experiments show that under different Non-IID experiment settings, our method can reduce the upload communication cost to about 2. 9% to 18. 9% of the conventional federated learning algorithm when the sparse rate is 0. 01.
Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation
Unlike prior works which mostly ignore the impact of near-field beam-split (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB.
PAMI: partition input and aggregate outputs for model interpretation
There is an increasing demand for interpretation of model predictions especially in high-risk applications.
Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification
To mitigate the resolution degradation issue and mine identity-sensitive cues from human faces, we propose to restore the missing facial details using prior facial knowledge, which is then propagated to a smaller network.
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model
For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks.
RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL
To address the challenge, we present RobustAnalog, a robust circuit design framework that involves the variation information in the optimization process.
Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation
This paper addresses two major challenges in terahertz (THz) channel estimation: the beam-split phenomenon, i. e., beam misalignment because of frequency-independent analog beamformers, and computational complexity because of the usage of ultra-massive number of antennas to compensate propagation losses.
Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks
Without channel estimation, the proposed approach exhibits approximately 60% improvement in image and speech classification tasks for diverse scenarios such as millimeter wave and IEEE 802. 11p vehicular channels.
Supervised Contrastive CSI Representation Learning for Massive MIMO Positioning
Similarity metric is crucial for massive MIMO positioning utilizing channel state information~(CSI).
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning
Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR).
Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer
Therefore, we propose a transformer-based Pose-guided Feature Disentangling (PFD) method by utilizing pose information to clearly disentangle semantic components (e. g. human body or joint parts) and selectively match non-occluded parts correspondingly.
Semi-supervised t-SNE for Millimeter-wave Wireless Localization
We consider the mobile localization problem in future millimeter-wave wireless networks with distributed Base Stations (BSs) based on multi-antenna channel state information (CSI).
Preserving Dense Features for Ki67 Nuclei Detection
Nuclei detection is a key task in Ki67 proliferation index estimation in breast cancer images.
RoDesigner: Variation-Aware Optimization for Robust Analog Design with Multi-Task RL
Specifically, circuit optimizations under different variations are considered as a set of tasks.
Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual Features in Images
In this paper, a simple yet effective optimization method is proposed to interpret the activation of any kernel of interest in CNN models.
Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain
Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s).
Joint Embedding in Named Entity Linking on Sentence Level
The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document.
Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains
Implicit discourse relation classification is one of the most difficult tasks in discourse parsing.
A Hybrid Model for Globally Coherent Story Generation
The model exploits a symbolic text planning module to produce text plans, thus reducing the demand of data; a neural surface realization module then generates fluent text conditioned on the text plan.
Self-Refining Deep Symmetry Enhanced Network for Rain Removal
Rain removal aims to extract and remove rain streaks from images.
Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification
Implicit discourse relation classification is one of the most difficult steps in discourse parsing.
Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification
Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connective as strong linguistic cues.
Accelerating Incremental Gradient Optimization with Curvature Information
This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems.
Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification
Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives.
General Classification
Implicit Discourse Relation Classification
+3
Curvature-aided Incremental Aggregated Gradient Method
We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method.
A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and non-smooth terms.
On the Need of Cross Validation for Discourse Relation Classification
The task of implicit discourse relation classification has received increased attention in recent years, including two CoNNL shared tasks on the topic.
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes
A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs.
An Efficient FPGA Implementation for 2-D MUSIC Algorithm
Multiple signal classification (MUSIC) algorithm is widely used in measuring the direction of arrival.
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization
In this paper, we develop a decentralized algorithm for the consensus optimization problem $$\min\limits_{x\in\mathbb{R}^p}~\bar{f}(x)=\frac{1}{n}\sum\limits_{i=1}^n f_i(x),$$ which is defined over a connected network of $n$ agents, where each function $f_i$ is held privately by agent $i$ and encodes the agent's data and objective.
Optimization and Control
