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Found 29 papers, 6 papers with code
Attention Fusion: a light yet efficient late fusion mechanism for task adaptation in NLU
However, given the trend of larger pre-trained models, fine-tuning these models for each downstream task is parameter-inefficient and computationally-expensive deeming this approach sub-optimal for adoption by NLU systems.
Time Series Language Model for Descriptive Caption Generation
The automatic generation of representative natural language descriptions for observable patterns in time series data enhances interpretability, simplifies analysis and increases cross-domain utility of temporal data.
Optimizing SSD Caches for Cloud Block Storage Systems Using Machine Learning Approaches
The management of data writes to SSD caches plays a crucial role in improving overall system performance, reducing latency, and extending the lifespan of storage devices.
Dynamic Adaptation in Data Storage: Real-Time Machine Learning for Enhanced Prefetching
The exponential growth of data storage demands has necessitated the evolution of hierarchical storage management strategies [1].
Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques
Traditional heuristics employed for storage performance optimization often fail to adapt to the variability and complexity of contemporary workloads, leading to significant performance bottlenecks and resource inefficiencies.
Wearable intelligent throat enables natural speech in stroke patients with dysarthria
Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments.
Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorers
Despite previous image restoration (IR) methods have often concentrated on isolated degradations, recent research has increasingly focused on addressing composite degradations involving a complex combination of multiple isolated degradations.
Training Data Attribution: Was Your Model Secretly Trained On Data Created By Mine?
Our experiments demonstrate that our method achieves an accuracy of over 80\% in identifying the source of a suspicious model's training data, without interfering the original training or generation process of the source model.
HAIR: Hypernetworks-based All-in-One Image Restoration
To alleviate this issue, we propose HAIR, a Hypernetworks-based All-in-One Image Restoration plug-and-play method that generates parameters based on the input image and thus makes the model to adapt to specific degradation dynamically.
Ranked #3 on
Blind All-in-One Image Restoration
on 3-Degradations
Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System
This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL).
Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated
With the rise of various online and mobile payment systems, transaction fraud has become a significant threat to financial security.
Credit Card Fraud Detection Using Advanced Transformer Model
With the proliferation of various online and mobile payment systems, credit card fraud has emerged as a significant threat to financial security.
Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising
This paper proposes new methods to enhance click-through rate (CTR) prediction models using the Deep Interest Network (DIN) model, specifically applied to the advertising system of Alibaba's Taobao platform.
Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology
This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance.
Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding
This paper explores the integration of strategic optimization methods in search advertising, focusing on ad ranking and bidding mechanisms within E-commerce platforms.
Infrared Image Super-Resolution via Lightweight Information Split Network
A key innovation within this model is the introduction of the Lightweight Information Split Block (LISB) for deep feature extraction.
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
Instilling Type Knowledge in Language Models via Multi-Task QA
Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge -- their types.
DAME: Domain Adaptation for Matching Entities
We study the zero-shot learning case on the target domain, and demonstrate that our method learns the EM task and transfers knowledge to the target domain.
Recurrent Neural Network from Adder's Perspective: Carry-lookahead RNN
The recurrent network architecture is a widely used model in sequence modeling, but its serial dependency hinders the computation parallelization, which makes the operation inefficient.
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs.
Towards Semi-Supervised Semantics Understanding from Speech
Much recent work on Spoken Language Understanding (SLU) falls short in at least one of three ways: models were trained on oracle text input and neglected the Automatics Speech Recognition (ASR) outputs, models were trained to predict only intents without the slot values, or models were trained on a large amount of in-house data.
Semantic Labeling Using a Deep Contextualized Language Model
Generating schema labels automatically for column values of data tables has many data science applications such as schema matching, and data discovery and linking.
Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding
The light encoder architecture separates the shared pre-trained networks from the mappings of generally encoded knowledge to specific domains of SLU, allowing for the domain adaptation to be performed solely at the light encoder and thus increasing efficiency.
A Fast Randomized Algorithm for Finding the Maximal Common Subsequences
A well-known result states that finding a Longest Common Subsequence (LCS) for $L$ strings is NP-hard, e. g., the computational complexity is exponential in $L$.
A Lightweight Algorithm to Uncover Deep Relationships in Data Tables
The key to our approach is a computationally lightweight forward addition algorithm that we developed to recursively extract the functional dependencies between table columns that are scalable to tables with many columns.
Gauss-Manin connection in disguise: Genus two curves
We describe an algebra of meromorphic functions on the Siegel domain of genus two which contains Siegel modular forms for an arithmetic index six subgroup of the symplectic group and it is closed under three canonical derivations of the Siegel domain.
Algebraic Geometry Mathematical Physics Complex Variables Mathematical Physics
Augmented Data Science: Towards Industrialization and Democratization of Data Science
Conversion of raw data into insights and knowledge requires substantial amounts of effort from data scientists.
Learning Robust Dialog Policies in Noisy Environments
In this paper, we focus on learning robust dialog policies to recover from these errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
