Search Results for author:
Found 26 papers, 9 papers with code
Gene-associated Disease Discovery Powered by Large Language Models
The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.
Intelligent Virtual Assistants with LLM-based Process Automation
This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests.
Leveraging sinusoidal representation networks to predict fMRI signals from EEG
The ability to obtain fMRI information from EEG would enable cost-effective, imaging across a wider set of brain regions.
TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds
In this work, we study TpopT (TemPlate OPTimization) as an alternative scalable framework for detecting low-dimensional families of signals which maintains high interpretability.
Controllable Data Generation Via Iterative Data-Property Mutual Mappings
To address these challenges, in this paper, we propose a general framework to enhance VAE-based data generators with property controllability and ensure disentanglement.
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
This work aims to serve as a valuable resource for understanding the potential and opportunities of HKG in health research.
Domain Generalization Deep Graph Transformation
As a result, domain generalization graph transformation that predicts graphs not available in the training data is under-explored, with multiple key challenges to be addressed including (1) the extreme space complexity when training on all input-output mode combinations, (2) difference of graph topologies between the input and the output modes, and (3) how to generalize the model to (unseen) target domains that are not in the training data.
Full Scaling Automation for Sustainable Development of Green Data Centers
The rapid rise in cloud computing has resulted in an alarming increase in data centers' carbon emissions, which now accounts for >3% of global greenhouse gas emissions, necessitating immediate steps to combat their mounting strain on the global climate.
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e. g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks.
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i. e., the forecasts should satisfy the hierarchical aggregation constraints.
A Graph Regularized Point Process Model For Event Propagation Sequence
Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals.
Multi-objective Deep Data Generation with Correlated Property Control
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.
Controllable Data Generation by Deep Learning: A Review
This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation.
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.
Sample Recycling for Nested Simulation with Application in Portfolio Risk Measurement
The resulting sample of conditional expectations is then used to estimate different risk measures of interest.
Deep Generative Model for Periodic Graphs
To address them, this paper proposes Periodical-Graph Disentangled Variational Auto-encoder (PGD-VAE), a new deep generative models for periodic graphs that can automatically learn, disentangle, and generate local and global graph patterns.
Global Regular Network for Writer Identification
The proposed GRN has two attributions: one is adding a branch to extract features contained in page; the other is using residual attention network to extract local feature.
GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Graph generation, which learns from known graphs and discovers novel graphs, has great potential in numerous research topics like drug design and mobility synthesis and is one of the fastest-growing domains recently due to its promise for discovering new knowledge.
A Metamodel Structure For Regression Analysis: Application To Prediction Of Autism Spectrum Disorder Severity
Traditional regression models do not generalize well when learning from small and noisy datasets.
Quantum walks on a programmable two-dimensional 62-qubit superconducting processor
Quantum walks are the quantum mechanical analogue of classical random walks and an extremely powerful tool in quantum simulations, quantum search algorithms, and even for universal quantum computing.
Quantum Physics
Experimental characterization of quantum many-body localization transition
Here, we experimentally implement a scalable protocol for detecting the many-body localization transition point, using the dynamics of a $N=12$ superconducting qubit array.
Quantum Physics Mesoscale and Nanoscale Physics Strongly Correlated Electrons
FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing
We quantify the strength of our proposed framework in different cases and compare it to the existing data-driven approaches.
