Search Results for author:
Found 18 papers, 7 papers with code
CovNet: Covariance Information-Assisted CSI Feedback for FDD Massive MIMO Systems
In this paper, we propose a novel covariance information-assisted channel state information (CSI) feedback scheme for frequency-division duplex (FDD) massive multi-input multi-output (MIMO) systems.
Is Locational Marginal Price All You Need for Locational Marginal Emission?
Growing concerns over climate change call for improved techniques for estimating and quantifying the greenhouse gas emissions associated with electricity generation and transmission.
Guiding Through Complexity: What Makes Good Supervision for Hard Reasoning Tasks?
Specifically, training on hard task supervision with the same outcome error rates but disparate step-wise error rates can lead to a 30\% accuracy gap on MATH benchmark.
MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks
We evaluate a wide variety of frontier vision-language models on MEGA-Bench to understand their capabilities across these dimensions.
Efficient Unit Commitment Constraint Screening under Uncertainty
We verify the proposed method's performance on a variety of UC setups and uncertainty situations.
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
The main barrier is the lack of large-scale human-annotated dataset.
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse domains.
Expert-Token Resonance: Redefining MoE Routing through Affinity-Driven Active Selection
(3) Theoretical derivation and experimental evidence of reduced expert capacity bounds under dynamic token distribution evolution.
MANTIS: Interleaved Multi-Image Instruction Tuning
We further evaluate Mantis on single-image benchmarks and demonstrate that Mantis also maintains a strong single-image performance on par with CogVLM and Emu2.
Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head
End-to-end transformer-based detectors (DETRs) have shown exceptional performance in both closed-set and open-vocabulary object detection (OVD) tasks through the integration of language modalities.
Open Vocabulary Object Detection
Real-Time Object Detection
+1
EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving
This paper introduces the task of Auditory Referring Multi-Object Tracking (AR-MOT), which dynamically tracks specific objects in a video sequence based on audio expressions and appears as a challenging problem in autonomous driving.
LocMoE: A Low-Overhead MoE for Large Language Model Training
However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity.
Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach
In this paper, we propose a novel planning and operation model minimizing the system-level carbon emissions via sitting and operating geographically shiftable resources.
Beam-Delay Domain Channel Estimation for mmWave XL-MIMO Systems
This paper investigates the uplink channel estimation of the millimeter-wave (mmWave) extremely large-scale multiple-input-multiple-output (XL-MIMO) communication system in the beam-delay domain, taking into account the near-field and beam-squint effects due to the transmission bandwidth and array aperture growth.
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.
S$^3$-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer for Monocular 3D Object Detection
These methods typically use visual and depth representations to generate query points on objects, whose quality plays a decisive role in the detection accuracy.
SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection
To tackle this problem, this paper proposes a novel "Supervised Scale-aware Deformable Attention" (SSDA) for monocular 3D object detection.
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.
