Search Results for author:
Found 23 papers, 5 papers with code
Machine learning empowered Modulation detection for OFDM-based signals
Unlike previous works that assume an ideal environment with precise knowledge of subcarrier count and cyclic prefix location, we consider blind modulation detection while accounting for realistic environmental parameters and imperfections.
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI
In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution.
$\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
To this end, we propose Semantic Space Informed Prompt learning with LLM ($S^2$IP-LLM) to align the pre-trained semantic space with time series embeddings space and perform time series forecasting based on learned prompts from the joint space.
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
To address this issue, we propose a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS forecasting within a continual learning paradigm, which leverages structural knowledge to steer the forecasting model toward identifying and adapting to different regimes, and selects representative MTS samples from each regime for memory replay.
Empowering Time Series Analysis with Large Language Models: A Survey
Specifically, we first state the challenges and motivations of applying language models in the context of time series as well as brief preliminaries of LLMs.
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.
Structural block driven - enhanced convolutional neural representation for relation extraction
In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning.
The Shift to 6G Communications: Vision and Requirements
The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements.
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-\textit{k} selection to improve communication efficiency.
An RNN-Survival Model to Decide Email Send Times
We posit that emails are likely to be opened sooner when send times are convenient for recipients, while for other send times, emails can get ignored.
Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction
Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks.
Nearly-Unsupervised Hashcode Representations for Relation Extraction
Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks.
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning
Many solutions proposed in the literature are reported to have high accuracy but are ineffective in real applications due to the non-representativity of the dataset used for training and evaluation of the underlying models.
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis
In the paper, we elaborate on the reason of multidimensional analysis problem in the context of molecular signaling, and next, we introduce the model of choice, simulation details and obtained time series dynamics.
Learning Non-Stationary Space-Time Models for Environmental Monitoring
One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena.
Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots
We propose an adaptive solution for the problem where stochastic real-world dynamics are modeled as a Gaussian Process (GP).
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact
Therefore, for an efficient yet accurate inference, we propose to build an induced latent dynamics representation using a novel algorithm LISAL that adaptively maximizes entropy or mutual information on the induced latent dynamics and marginal likelihood of observed real dynamics in an iterative manner.
Adaptive Sensing for Learning Nonstationary Environment Models
The core idea in LISAL is to learn two models using Gaussian processes (GPs) wherein the first is a nonstationary GP directly modeling the phenomenon.
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses.
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling
Natural language processing often involves computations with semantic or syntactic graphs to facilitate sophisticated reasoning based on structural relationships.
Kernelized Hashcode Representations for Relation Extraction
Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods.
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations.
