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Found 159 papers, 57 papers with code
Qlib: An AI-oriented Quantitative Investment Platform
Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.
ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting
Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field.
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data.
GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework
Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations.
MuseCoco: Generating Symbolic Music from Text
In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements.
EmoGen: Eliminating Subjective Bias in Emotional Music Generation
In this paper, we propose EmoGen, an emotional music generation system that leverages a set of emotion-related music attributes as the bridge between emotion and music, and divides the generation into two stages: emotion-to-attribute mapping with supervised clustering, and attribute-to-music generation with self-supervised learning.
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models
For developers and amateurs, it is very difficult to grasp all of these task to satisfy their requirements in music processing, especially considering the huge differences in the representations of music data and the model applicability across platforms among various tasks.
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution.
Ranked #3 on
Atari Games
on Atari 2600 Skiing
(using extra training data)
VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing
In this paper, we propose a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech.
NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers
To enhance the zero-shot capability that is important to achieve diverse speech synthesis, we design a speech prompting mechanism to facilitate in-context learning in the diffusion model and the duration/pitch predictor.
Deep Subdomain Adaptation Network for Image Classification
The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network
Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting.
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations
While adopting complex GNN models with more informative message passing and aggregation mechanisms can obviously benefit heterogeneous vertex representations and cooperative policy learning, it could, on the other hand, increase the training difficulty of MARL and demand more intense and direct reward signals compared to the original global reward.
Invertible Image Rescaling
High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images.
BatteryML:An Open-source platform for Machine Learning on Battery Degradation
Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions.
SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System
With the increasing diversity of ML infrastructures nowadays, distributed training over heterogeneous computing systems is desired to facilitate the production of big models.
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Most existing event extraction (EE) methods merely extract event arguments within the sentence scope.
Ranked #4 on
Document-level Event Extraction
on ChFinAnn
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond
Then, to understand the interpretation results, we also survey the performance metrics for evaluating interpretation algorithms.
HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information
To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.
Dual Supervised Learning
Many supervised learning tasks are emerged in dual forms, e. g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation.
Clinical Relation Extraction Using Transformer-based Models
The goal of this study is to systematically explore three widely used transformer-based models (i. e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain.
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.
MC-BERT: Efficient Language Pre-Training via a Meta Controller
Pre-trained contextual representations (e. g., BERT) have become the foundation to achieve state-of-the-art results on many NLP tasks.
ResiDual: Transformer with Dual Residual Connections
In this paper, we propose ResiDual, a novel Transformer architecture with Pre-Post-LN (PPLN), which fuses the connections in Post-LN and Pre-LN together and inherits their advantages while avoids their limitations.
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Stock trend prediction plays a critical role in seeking maximized profit from stock investment.
Ranked #16 on
Stock Market Prediction
on Astock
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort.
Me LLaMA: Foundation Large Language Models for Medical Applications
In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets.
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting.
Empowering Diffusion Models on the Embedding Space for Text Generation
Diffusion models have achieved state-of-the-art synthesis quality on both visual and audio tasks, and recent works further adapt them to textual data by diffusing on the embedding space.
Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
In this paper, we propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps for multivariate time series forecasting.
A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management
Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment.
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem
We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP).
Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem
Traveling Salesman Problem (TSP), as a classic routing optimization problem originally arising in the domain of transportation and logistics, has become a critical task in broader domains, such as manufacturing and biology.
A Study of Generative Large Language Model for Medical Research and Healthcare
This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare.
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering.
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series discrepancy.
ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples.
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
However, the effective utilization of their strong modeling ability in the probabilistic time series forecasting task remains an open question, partially due to the challenge of instability arising from their stochastic nature.
UADB: Unsupervised Anomaly Detection Booster
This is a challenging task given the heterogeneous model structures and assumptions adopted by existing UAD methods.
SHGNN: Structure-Aware Heterogeneous Graph Neural Network
Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.
TiC: Exploring Vision Transformer in Convolution
While models derived from Vision Transformers (ViTs) have been phonemically surging, pre-trained models cannot seamlessly adapt to arbitrary resolution images without altering the architecture and configuration, such as sampling the positional encoding, limiting their flexibility for various vision tasks.
AA-Forecast: Anomaly-Aware Forecast for Extreme Events
Moreover, the framework employs a dynamic uncertainty optimization algorithm that reduces the uncertainty of forecasts in an online manner.
GLARE: A Dataset for Traffic Sign Detection in Sun Glare
It provides an essential enrichment to the widely used LISA Traffic Sign dataset.
Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
Class-imbalance is a common problem in machine learning practice.
On the Generalization Properties of Diffusion Models
This precisely elucidates the adverse effect of "modes shift" in ground truths on the model generalization.
Light Multi-segment Activation for Model Compression
Inspired by the nature of the expressiveness ability in Neural Networks, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model.
Learning Physics-Informed Neural Networks without Stacked Back-propagation
In this work, we develop a novel approach that can significantly accelerate the training of Physics-Informed Neural Networks.
Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features
While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled samples helps to improve the generalization power.
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets
There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies.
Dynamic Graph Representation Learning with Fourier Temporal State Embedding
In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.
ReLF: Scalable Remote Live Forensics for Android
The world has witnessed the proliferation of mobile technologies as well as smartphone-related cybercrimes in recent years.
Mildly Constrained Evaluation Policy for Offline Reinforcement Learning
Offline reinforcement learning (RL) methodologies enforce constraints on the policy to adhere closely to the behavior policy, thereby stabilizing value learning and mitigating the selection of out-of-distribution (OOD) actions during test time.
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process Download PDF
To address this challenge, we introduce a novel Multi-Granularity Time Series Diffusion (MG-TSD) model, which achieves state-of-the-art predictive performance by leveraging the inherent granularity levels within the data as given targets at intermediate diffusion steps to guide the learning process of diffusion models.
A survey on trajectory clustering analysis
This paper comprehensively surveys the development of trajectory clustering.
CSWA: Aggregation-Free Spatial-Temporal Community Sensing
In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}.
Slim-DP: A Light Communication Data Parallelism for DNN
However, with the increasing size of DNN models and the large number of workers in practice, this typical data parallelism cannot achieve satisfactory training acceleration, since it usually suffers from the heavy communication cost due to transferring huge amount of information between workers and the parameter server.
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks
In this framework, we propose to aggregate the local models by ensemble, i. e., averaging the outputs of local models instead of the parameters.
FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification
Classical LDA for EHR data classification, however, suffers from two handicaps: the ill-posed estimation of LDA parameters (e. g., covariance matrix), and the "linear inseparability" of EHR data.
Improving Covariance-Regularized Discriminant Analysis for EHR-based Predictive Analytics of Diseases
In this paper, we revised the problem of predictive analysis of disease using personal EHR data and LDA classifier.
Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding
Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries.
KNET: A General Framework for Learning Word Embedding using Morphological Knowledge
In particular, we introduce a novel neural network architecture called KNET that leverages both contextual information and morphological word similarity built based on morphological knowledge to learn word embeddings.
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
Click prediction is one of the fundamental problems in sponsored search.
WordRep: A Benchmark for Research on Learning Word Representations
In this paper, we describe the details of the WordRep collection and show how to use it in different types of machine learning research related to word embedding.
Building Computational Models to Predict One-Year Mortality in ICU Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome
Heart disease remains the leading cause of death in the United States.
TabNN: A Universal Neural Network Solution for Tabular Data
Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all.
Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome
Predicting the risk of mortality for patients with acute myocardial infarction (AMI) using electronic health records (EHRs) data can help identify risky patients who might need more tailored care.
Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter
Among American women, the rate of breast cancer is only second to lung cancer.
Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States
We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).
LightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm
Instead of using fixed coding matrix and decoding strategy, LightMC uses a differentiable decoding strategy, which enables it to dynamically optimize the coding matrix and decoding strategy, toward increasing the overall accuracy of multiclass classification, via back propagation jointly with the training of base learners in an iterative way.
Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods
Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive measures are potential solutions to improve cancer patients's quality of life.
Federated Learning for Healthcare Informatics
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others.
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets
Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets, although there was no substantial difference among the strategies tested.
Measuring Model Complexity of Neural Networks with Curve Activation Functions
Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.
Learning to Reweight with Deep Interactions
Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc.
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks
In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch.
COSEA: Convolutional Code Search with Layer-wise Attention
However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes.
Applications of artificial intelligence in drug development using real-world data
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development.
REST: Relational Event-driven Stock Trend Forecasting
To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.
Model Complexity of Deep Learning: A Survey
Model complexity is a fundamental problem in deep learning.
Universal Trading for Order Execution with Oracle Policy Distillation
As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.
A Conversational Agent System for Dietary Supplements Use
Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively.
Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study
We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020.
Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter
We chose the best performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (i. e., iDISK).
Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions
Whole genome sequencing (WGS) is quickly becoming the customary means for identification of antimicrobial resistance (AMR) due to its ability to obtain high resolution information about the genes and mechanisms that are causing resistance and driving pathogen mobility.
Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation
Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance.
A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models
In this study, we examined two state-of-the-art transformer-based NLP models, including BERT and RoBERTa, to extract SBDoH concepts from clinical narratives, applied the best performing model to extract SBDoH concepts on a lung cancer screening patient cohort, and examined the difference of SBDoH information between NLP extracted results and structured EHRs (SBDoH information captured in standard vocabularies such as the International Classification of Diseases codes).
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management
We consider inventory management (IM) problem for a single store with a large number of SKUs (stock keeping units) in this paper, where we need to make replenishment decisions for each SKU to balance its supply and demand.
Deep Ensemble Policy Learning
Ensemble learning, which can consistently improve the prediction performance in supervised learning, has drawn increasing attentions in reinforcement learning (RL).
An Analysis of WordNet’s Coverage of Gender Identity Using Twitter and The National Transgender Discrimination Survey
While gender identities in the Western world are typically regarded as binary, our previous work (Hicks et al., 2015) shows that there is more lexical variety of gender identity and the way people identify their gender.
Independence-aware Advantage Estimation
Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon.
Demonstration Actor Critic
One approach leverages demonstration data in a supervised manner, which is simple and direct, but can only provide supervision signal over those states seen in the demonstrations.
AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation
As a specific semantic segmentation task, aerial imagery segmentation has been widely employed in high spatial resolution (HSR) remote sensing images understanding.
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting
How to discover the useful implicit relation between entities and effectively utilize the relations for each entity under various circumstances is crucial.
Temporal Subtyping of Alzheimer's Disease Using Medical Conditions Preceding Alzheimer's Disease Onset in Electronic Health Records
Subtyping of Alzheimer's disease (AD) can facilitate diagnosis, treatment, prognosis and disease management.
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
GatorTron models scale up the clinical language model from 110 million to 8. 9 billion parameters and improve 5 clinical NLP tasks (e. g., 9. 6% and 9. 5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery.
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble
Considering the great performance of ensemble methods on both accuracy and generalization in supervised learning (SL), we design a robust and applicable method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner.
A Simple yet Effective Framework for Active Learning to Rank
To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understanding users' queries, retrieve relevant contents from a pool of trillions of webpages, and rank the most relevant webpages on the top of results.
LordNet: Learning to Solve Parametric Partial Differential Equations without Simulated Data
Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE).
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures
It uses less than 5% FLOPS compared with previous SOTA methods on the largest benchmark dataset.
Joint Application of the Target Trial Causal Framework and Machine Learning Modeling to Optimize Antibiotic Therapy: Use Case on Acute Bacterial Skin and Skin Structure Infections due to Methicillin-resistant Staphylococcus aureus
ABSSSI-MRSA is a challenging condition with reduced treatment options - vancomycin is the preferred choice, but it has non-negligible side effects.
Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Data
Estimating treatment effects, especially individualized treatment effects (ITE), using observational data is challenging due to the complex situations of confounding bias.
P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos
While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging.
Learning Differential Operators for Interpretable Time Series Modeling
In this work, we propose an learning framework that can automatically obtain interpretable PDE models from sequential data.
Multi-Objective Personalized Product Retrieval in Taobao Search
These problems promote us to further strengthen the capabilities of our EBR model in both relevance estimation and personalized retrieval.
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
To reduce the compute time overhead of private learning, we show that \emph{per-layer clipping}, where the gradient of each neural network layer is clipped separately, allows clipping to be performed in conjunction with backpropagation in differentially private optimization.
SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies
Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i. e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i. e., opioid use), and evaluate the extraction rate of SDoH using cancer populations.
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management
In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand.
Regeneration Learning: A Learning Paradigm for Data Generation
Regeneration learning extends the concept of representation learning to data generation tasks, and can be regarded as a counterpart of traditional representation learning, since 1) regeneration learning handles the abstraction (Y') of the target data Y for data generation while traditional representation learning handles the abstraction (X') of source data X for data understanding; 2) both the processes of Y'-->Y in regeneration learning and X-->X' in representation learning can be learned in a self-supervised way (e. g., pre-training); 3) both the mappings from X to Y' in regeneration learning and from X' to Y in representation learning are simpler than the direct mapping from X to Y.
A Study on ReLU and Softmax in Transformer
This paper sheds light on the following points: 1) Softmax and ReLU use different normalization methods over elements which lead to different variances of results, and ReLU is good at dealing with a large number of key-value slots; 2) FFN and key-value memory are equivalent, and thus the Transformer can be viewed as a memory network where FFNs and self-attention networks are both key-value memories.
Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks
Due to the high similarity between MRI data and videos, we conduct extensive empirical studies on video recognition techniques for MRI classification to answer the questions: (1) can we directly use video recognition models for MRI classification, (2) which model is more appropriate for MRI, (3) are the common tricks like data augmentation in video recognition still useful for MRI classification?
DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data
DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii) an information-theoretic generative adversarial network (Info-GAN) to generate counterfactuals; (iii) a doubly robust block incorporating treatment propensities for outcome predictions.
Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures
Materials and methods: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes.
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension
GatorTron-MRC achieves the best strict and lenient F1-scores for concept extraction, outperforming previous deep learning models on the two datasets by 1%~3% and 0. 7%~1. 3%, respectively.
Clinical Concept Extraction
Machine Reading Comprehension
+3
HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Details
3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and animatable 3D facial surfaces from a single image.
Ranked #1 on
3D Face Reconstruction
on REALY (side-view)
Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing
To assist in the diagnosis and phenotyping of delirium, we formed an expert panel to categorize diverse delirium symptoms, composed annotation guidelines, created a delirium corpus with diverse delirium symptoms, and developed NLP methods to extract delirium symptoms from clinical notes.
Deliberate then Generate: Enhanced Prompting Framework for Text Generation
Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts.
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance
Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets.
Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI
The time needed to answer questions related to the content of abstracts was significantly lower in groups two and three compared to the first group using full abstracts.
Pre-Trained Large Language Models for Industrial Control
2)~How well can GPT-4 generalize to different scenarios for HVAC control?
Apple Vision Pro for Healthcare: "The Ultimate Display"? -- Entering the Wonderland of Precision Medicine
At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the Vision Pro.
Microstructure-Empowered Stock Factor Extraction and Utilization
To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios.
Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction
In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction.
PromptTTS 2: Describing and Generating Voices with Text Prompt
TTS approaches based on the text prompt face two main challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompts for speech.
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)
Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications.
Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial
Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4.
Accurate battery lifetime prediction across diverse aging conditions with deep learning
Here we introduce a universal deep learning approach that is capable of accommodating various aging conditions and facilitating effective learning under low-resource conditions by leveraging data from rich conditions.
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction
We evaluated the transfer learning ability of the prompt-based learning algorithms in a cross-institution setting.
Position Paper: An Integrated Perspective on Data, Metrics, and Methodology for Deep Time-Series Forecasting
Deep time-series forecasting plays an integral role in numerous practical applications.
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining
In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.
Towards Foundation Models for Learning on Tabular Data
Despite considerable efforts in developing effective learning models for tabular data, current transferable tabular models remain in their infancy, limited by either the lack of support for direct instruction following in new tasks or the neglect of acquiring foundational knowledge and capabilities from diverse tabular datasets.
On the Impact of Cross-Domain Data on German Language Models
Traditionally, large language models have been either trained on general web crawls or domain-specific data.
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle
In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making.
GAIA: Zero-shot Talking Avatar Generation
Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image.
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need
Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.
Morphological Profiling for Drug Discovery in the Era of Deep Learning
Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.
Work-in-progress: a deep learning strategy for I/O scheduling in storage systems
Under the big data era, there is a crucial need to improve the performance of storage systems for data-intensive applications.
Addressing Distribution Shift in Time Series Forecasting with Instance Normalization Flows
Due to non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting.
XTSFormer: Cross-Temporal-Scale Transformer for Irregular Time Event Prediction
Event prediction aims to forecast the time and type of a future event based on a historical event sequence.
UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing
Recent advances in text-guided video editing have showcased promising results in appearance editing (e. g., stylization).
Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data
We constructed an interactive knowledge map to disseminate our study results.
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training
Accordingly, permutation on the training data is considered as a potential solution, since this can make the model predict antecedent words or tokens.
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
Specifically, 1) we design a neural codec with factorized vector quantization (FVQ) to disentangle speech waveform into subspaces of content, prosody, timbre, and acoustic details; 2) we propose a factorized diffusion model to generate attributes in each subspace following its corresponding prompt.
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship.
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning
The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives.
Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning
We examined the prompt-tuning strategies, the size of soft prompts, and the few-short learning ability of GatorTronGPT, a generative clinical LLM developed using 277 billion clinical and general English words with up to 20 billion parameters.
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa.
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency.
RD2Bench: Toward Data-Centric Automatic R&D
The progress of humanity is driven by those successful discoveries accompanied by countless failed experiments.
Empowering Large Language Models on Robotic Manipulation with Affordance Prompting
While large language models (LLMs) are successful in completing various language processing tasks, they easily fail to interact with the physical world by generating control sequences properly.
©Plug-in Authorization for Human Content Copyright Protection in Text-to-Image Model
To mitigate this, we propose the \copyright Plug-in Authorization framework, introducing three operations: addition, extraction, and combination.
Protecting Your LLMs with Information Bottleneck
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content.
