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Found 38 papers, 13 papers with code
Uncovering Cross-Domain Recommendation Ability of Large Language Models
To bridge this gap, we propose LLM4CDR, a novel CDR pipeline that constructs context-aware prompts by leveraging users' purchase history sequences from a source domain along with shared features between source and target domains.
Foundation Models for CPS-IoT: Opportunities and Challenges
Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things (IoT), replacing mechanistic and basic statistical models with those derived from data.
Transforming the Hybrid Cloud for Emerging AI Workloads
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co-design approaches, emphasizing usability, manageability, affordability, adaptability, efficiency, and scalability.
MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT
However, such a setting is impractical in real-world IoT sensing applications where data is typically collected by distributed nodes with heterogeneous data modalities, and is also rarely labeled.
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning
This paper addresses the problem of optimizing the allocation of labeling resources for semi-supervised belief representation learning in social networks.
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs
The optimization places the generated nodes in a manner that: (1) minimizes the classification loss to guarantee training accuracy and (2) maximizes label propagation to low-confidence nodes in the downstream task to ensure high-quality propagation.
MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective
The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies.
On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study
This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications.
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
To incorporate sufficient diversity into the IoT training data, one therefore needs to consider a combinatorial explosion of training cases that are multiplicative in the number of objects considered and the possible environmental conditions in which such objects may be encountered.
InfoPattern: Unveiling Information Propagation Patterns in Social Media
Social media play a significant role in shaping public opinion and influencing ideological communities through information propagation.
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
Existing multimodal contrastive frameworks mostly rely on the shared information between sensory modalities, but do not explicitly consider the exclusive modality information that could be critical to understanding the underlying sensing physics.
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting
Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury.
Word Embeddings Are Steers for Language Models
In this work, we theoretically and empirically revisit output word embeddings and find that their linear transformations are equivalent to steering language model generation styles.
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning
This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones.
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set.
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs
Second, the potentially dynamic distributions from the initially observable facts to the future facts ask for explicitly modeling the evolving characteristics of new entities.
Phy-Taylor: Physics-Model-Based Deep Neural Networks
To do so, the PhN augments neural network layers with two key components: (i) monomials of Taylor series expansion of nonlinear functions capturing physical knowledge, and (ii) a suppressor for mitigating the influence of noise.
Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification
This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC).
RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph
And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction.
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving
Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks.
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e. g., posts that represent user views) into an appropriate disentangled latent space.
Controllable and Diverse Text Generation in E-commerce
The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing \textit{Apex} to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy.
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network Latency
With comprehensive evaluations, our system can consistently reduce end-to-end latency by 2× to 4× with 1% accuracy loss, compared to state-of-the-art neural network offloading systems.
Scheduling Real-time Deep Learning Services as Imprecise Computations
The paper presents an efficient real-time scheduling algorithm for intelligent real-time edge services, defined as those that perform machine intelligence tasks, such as voice recognition, LIDAR processing, or machine vision, on behalf of local embedded devices that are themselves unable to support extensive computations.
ControlVAE: Tuning, Analytical Properties, and Performance Analysis
ControlVAE is a new variational autoencoder (VAE) framework that combines the automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value.
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning
Existing methods, such as $\beta$-VAE and FactorVAE, assign a large weight to the KL-divergence term in the objective function, leading to high reconstruction errors for the sake of better disentanglement.
Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion
Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss.
Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US
We further compare policies that rely on partial venue closure to policies that espouse wide-spread periodic testing instead (i. e., in lieu of social distancing).
Physics and Society Computers and Society Social and Information Networks
ControlVAE: Controllable Variational Autoencoder
Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation learning.
paper2repo: GitHub Repository Recommendation for Academic Papers
Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\textit{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic.
Revisiting Over-smoothing in Deep GCNs
Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs).
Macross: Urban Dynamics Modeling based on Metapath Guided Cross-Modal Embedding
As the ongoing rapid urbanization takes place with an ever-increasing speed, fully modeling urban dynamics becomes more and more challenging, but also a necessity for socioeconomic development.
STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks
IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain.
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time.
RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations
Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence to a broad spectrum of mobile and ubiquitous applications.
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
It is thus able to shorten execution time by 71. 4% to 94. 5%, and decrease energy consumption by 72. 2% to 95. 7%.
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice.
