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Found 14 papers, 2 papers with code
Generalization Gap in Data Augmentation: Insights from Illumination
In the field of computer vision, data augmentation is widely used to enrich the feature complexity of training datasets with deep learning techniques.
Word Order's Impacts: Insights from Reordering and Generation Analysis
Existing works have studied the impacts of the order of words within natural text.
Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning
Rewriting is a common procedure in logic synthesis aimed at improving the performance, power, and area (PPA) of circuits.
Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent
However, existing methods are still not able to effectively and efficiently exploit relevant information from these auxiliary supplements to further unleash the power of the conversational agents and the language models they use.
MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation
Graph Edit Distance (GED) is a general and domain-agnostic metric to measure graph similarity, widely used in graph search or retrieving tasks.
Persona-Coded Poly-Encoder: Persona-Guided Multi-Stream Conversational Sentence Scoring
Recent advances in machine learning and deep learning have led to the widespread use of Conversational AI in many practical applications.
Persona-Based Conversational AI: State of the Art and Challenges
First, we provide a literature review focusing on the current state-of-the-art methods that utilize persona information.
Contextualized Scene Imagination for Generative Commonsense Reasoning
In this paper, we propose an Imagine-and-Verbalize (I&V) method, which learns to imagine a relational scene knowledge graph (SKG) with relations between the input concepts, and leverage the SKG as a constraint when generating a plausible scene description.
Empirical Quantitative Analysis of COVID-19 Forecasting Models
In this paper, we perform quantitative analysis of COVID-19 forecasting of confirmed cases and deaths across different regions in the United States with different forecasting horizons, and evaluate the relative impacts of the following three dimensions on the predictive performance (improvement and variation) through different evaluation metrics: model selection, hyperparameter tuning, and the length of time series required for training.
Intersecting Surface defects and 3d Superconformal indices
Finally we propose a q-Virasoro construction for the superconformal indices.
High Energy Physics - Theory
Elastic Bulk Synchronous Parallel Model for Distributed Deep Learning
To ameliorate this shortcoming of classic BSP, we propose ELASTICBSP a model that aims to relax its strict synchronization requirement.
Dynamic Graph Embedding via LSTM History Tracking
To handle large dynamic networks in downstream applications such as link prediction and anomaly detection, it is essential for such networks to be transferred into a low dimensional space.
Dynamic Stale Synchronous Parallel Distributed Training for Deep Learning
In this paper, we present a distributed paradigm on the parameter server framework called Dynamic Stale Synchronous Parallel (DSSP) which improves the state-of-the-art Stale Synchronous Parallel (SSP) paradigm by dynamically determining the staleness threshold at the run time.
Drug Selection via Joint Push and Learning to Rank
We have developed a new learning-to-rank method, denoted as pLETORg , that predicts drug ranking structures in each cell line via using drug latent vectors and cell line latent vectors.
