Search Results for author:
Found 20 papers, 7 papers with code
Benchmarking the Robustness of Optical Flow Estimation to Corruptions
Optical flow estimation is extensively used in autonomous driving and video editing.
EI-Nexus: Towards Unmediated and Flexible Inter-Modality Local Feature Extraction and Matching for Event-Image Data
Event cameras, with high temporal resolution and high dynamic range, have limited research on the inter-modality local feature extraction and matching of event-image data.
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
Our algorithm for linear mixture MDPs achieves a nearly minimax optimal regret upper bound of $\widetilde{\mathcal{O}}(d\sqrt{\mathrm{sp}(v^*)T})$ over $T$ time steps where $\mathrm{sp}(v^*)$ is the span of the optimal bias function $v^*$ and $d$ is the dimension of the feature mapping.
P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty
This paper presents P2U-SLAM, a visual Simultaneous Localization And Mapping (SLAM) system with a wide Field of View (FoV) camera, which utilizes pose uncertainty and point uncertainty.
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions.
Efficient Unit Commitment Constraint Screening under Uncertainty
We verify the proposed method's performance on a variety of UC setups and uncertainty situations.
Risk-Aware Value-Oriented Net Demand Forecasting for Virtual Power Plants
This paper develops a risk-aware net demand forecasting product for virtual power plants, which helps reduce the risk of high operation costs.
Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs
Our algorithm runs an optimistic value iteration on a discounted-reward MDP that approximates the average-reward setting.
Improving Sequential Market Coordination via Value-oriented Renewable Energy Forecasting
Numerical studies demonstrate that our forecasts significantly reduce overall operating costs for deterministic market clearing compared to conventional forecasts based on expected RES production.
Deriving Loss Function for Value-oriented Renewable Energy Forecasting
By integrating it into the upper-level objective for minimizing expected operation cost, we convert the bilevel problem to a single-level one and derive the loss function for training the model.
Fast Constraint Screening for Multi-Interval Unit Commitment
Extensive simulations on both specific load samples and load regions validate the proposed technique can screen out more than 80% constraints while preserving the feasibility of multi-interval UC problem.
Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach
To bridge the gap, we design a value-oriented point forecasting approach for sequential energy dispatch problems with renewable energy sources.
LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments
The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV.
Optimal Vehicle Charging in Bilevel Power-Traffic Networks via Charging Demand Function
Specifically, the power network determines the charging price at various locations, while EVs on the traffic network optimize the charging power given the price, acting as price-takers.
Blockchain Network Analysis: A Comparative Study of Decentralized Banks
We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound.
Enabling Fast Unit Commitment Constraint Screening via Learning Cost Model
Unit commitment (UC) are essential tools to transmission system operators for finding the most economical and feasible generation schedules and dispatch signals.
Efficient Demand Response Location Targeting for Price Spike Mitigation by Exploiting Price-demand Relationship
We formulate a bilevel program where the upper level determines the DR locations and demand reductions while ensuring the average nodal prices meet a predetermined target.
A Contextual Bandit Approach for Value-oriented Prediction Interval Forecasting
The numerical study regarding a two-timescale operation of a virtual power plant verifies the superiority of the proposed approach in terms of operational value.
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning
Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method.
DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation
To the best of our knowledge, we are the first to make a reasonable dynamic runtime scheduler on the combination of tensor swapping and tensor recomputation without user oversight.
