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Found 23 papers, 8 papers with code
Revisiting Skip-Gram Negative Sampling Model with Rectification
We revisit skip-gram negative sampling (SGNS), one of the most popular neural-network based approaches to learning distributed word representation.
Towards Practical Visual Search Engine within Elasticsearch
In this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine.
Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting
We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function.
Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines
A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines.
An Empirical Comparison of FAISS and FENSHSES for Nearest Neighbor Search in Hamming Space
In this paper, we compare the performances of FAISS and FENSHSES on nearest neighbor search in Hamming space--a fundamental task with ubiquitous applications in nowadays eCommerce.
DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances
This paper presents an observer-integrated Reinforcement Learning (RL) approach, called Disturbance OBserver Network (DOB-Net), for robots operating in environments where disturbances are unknown and time-varying, and may frequently exceed robot control capabilities.
Enjoy the Untrusted Cloud: A Secure, Scalable and Efficient SQL-like Query Framework for Outsourcing Data
On one hand, such work lacks of supporting scalable access control over multiple data users.
Cryptography and Security Databases Distributed, Parallel, and Cluster Computing
Deep Variational Luenberger-type Observer for Stochastic Video Prediction
Our model builds upon an variational encoder which transforms the input video into a latent feature space and a Luenberger-type observer which captures the dynamic evolution of the latent features.
GenCos' Behaviors Modeling Based on Q Learning Improved by Dichotomy
Q learning is widely used to simulate the behaviors of generation companies (GenCos) in an electricity market.
Vestigial anyon condensation in kagome quantum spin liquids
We construct a lattice model of topological order (kagome quantum spin liquids) and solve it with unbiased quantum Monte Carlo simulations.
Strongly Correlated Electrons
Global scheme of sweeping cluster algorithm to sample among topological sectors
Local constraint is closely related to the gauge field, so constrained models are usually effective low energy descriptions and important in condensed matter physics.
Statistical Mechanics Strongly Correlated Electrons
Channel-wise Knowledge Distillation for Dense Prediction
Observing that in semantic segmentation, some layers' feature activations of each channel tend to encode saliency of scene categories (analogue to class activation mapping), we propose to align features channel-wise between the student and teacher networks.
Phase Diagram of Triangular Lattice Quantum Ising Model under External Field
Quantum Ising model on a triangular lattice hosts a finite temperature Berezinskii-Kosterlitz-Thouless (BKT) phase with emergent U(1) symmetry, and it will transit into an up-up-down (UUD) phase with $C_3$ symmetry breaking upon an infinitesimal external field along the longitudinal direction, but the overall phase diagram spanned by the axes of external field and temperature remains opaque due to the lack of systematic invesitgations with controlled methodologies.
Strongly Correlated Electrons Statistical Mechanics
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior
By leveraging experience from previous tasks, meta-learning algorithms can achieve effective fast adaptation ability when encountering new tasks.
Physics-informed generative neural network: an application to troposphere temperature prediction
The generative neural network takes the mask as prior for the second-stage refined predictions.
Dynamic Flow Equilibrium of Transportation and Power Distribution Networks Considering Flexible Traveling Choices and Voltage Quality Improvement
The increasing global spread of electric vehicles (EVs) has introduced significant interdependence between transportation and power networks.
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making
The framework offers both a guarantee of generalized performance and good accuracy.
Revisiting Neural Retrieval on Accelerators
A key component of retrieval is to model (user, item) similarity, which is commonly represented as the dot product of two learned embeddings.
TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support
Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
Stochastic Optimization of Coupled Power Distribution-Urban Transportation Network Operations with Autonomous Mobility on Demand Systems
Then, a bi-level power-traffic flow (PTF) model is proposed to characterize the interaction of power distribution networks (PDNs) and urban transportation networks (UTNs).
RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence
Although federated learning (FL) is a promising tool to solve the problem, adapting conventional FL frameworks to VCI is nontrivial.
FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited Knowledge
Despite the initial success of current audio backdoor attacks, they suffer from the following limitations: (i) Most of them require sufficient knowledge, which limits their widespread adoption.
