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Found 25 papers, 18 papers with code
Exploring GPT's Ability as a Judge in Music Understanding
We convert the music data to symbolic inputs and evaluate LLMs' ability in detecting annotation errors in three key MIR tasks: beat tracking, chord extraction, and key estimation.
Estimating quantum relative entropies on quantum computers
Quantum relative entropy, a quantum generalization of the well-known Kullback-Leibler divergence, serves as a fundamental measure of the distinguishability between quantum states and plays a pivotal role in quantum information science.
Baichuan Alignment Technical Report
Baichuan-Instruct is an internal model, while Qwen2-Nova-72B and Llama3-PBM-Nova-70B are instruct versions of the Qwen2-72B and Llama-3-70B base models, optimized through Baichuan Alignment.
Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models
Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i. e., In-Distribution (InD), or from OoD.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
This combination of high performance, efficiency, and flexibility makes PAS a valuable system for enhancing the usability and effectiveness of LLMs through improved prompt engineering.
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation
We evaluated popular LLMs such as Llama, Baichuan, ChatGLM, and GPT models.
Revisiting Random Weight Perturbation for Efficiently Improving Generalization
Improving the generalization ability of modern deep neural networks (DNNs) is a fundamental challenge in machine learning.
Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
The integration of large language models (LLMs) and search engines represents a significant evolution in knowledge acquisition methodologies.
Kernel PCA for Out-of-Distribution Detection
Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective
Motivated by such diversities on OoD loss landscape across modes, we revisit the deep ensemble method for OoD detection through mode ensemble, leading to improved performance and benefiting the OoD detector with reduced variances.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
Efficient Generalization Improvement Guided by Random Weight Perturbation
To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability.
On Multi-head Ensemble of Smoothed Classifiers for Certified Robustness
Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple deep neural networks (DNNs) has shown state-of-the-art performances.
Online LiDAR-Camera Extrinsic Parameters Self-checking
This calibration task is multi-modal, where the rich color and texture information captured by the camera and the accurate three-dimensional spatial information from the LiDAR is incredibly significant for downstream tasks.
Unifying Gradients to Improve Real-world Robustness for Deep Networks
The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i. e., whether a DNN resists black-box adversarial attacks, among which score-based query attacks (SQAs) are most threatening since they can effectively hurt a victim network with the only access to model outputs.
CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene
Sensor-based environmental perception is a crucial part of the autonomous driving system.
Subspace Adversarial Training
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.
Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels
Random Fourier Features (RFF) demonstrate wellappreciated performance in kernel approximation for largescale situations but restrict kernels to be stationary and positive definite.
Test of the superdiffusion model in the interstellar medium around the Geminga pulsar
The TeV $\gamma$-ray halo around the Geminga pulsar is an important indicator of cosmic-ray (CR) propagation in the local zone of the Galaxy as it reveals the spatial distribution of the electrons and positrons escaping from the pulsar.
High Energy Astrophysical Phenomena High Energy Physics - Phenomenology
Towards Robust Neural Networks via Orthogonal Diversity
In this way, the proposed DIO augments the model and enhances the robustness of DNN itself as the learned features can be corrected by these mutually-orthogonal paths.
Learn Robust Features via Orthogonal Multi-Path
To defend adversarial attacks, we design a block containing multiple paths to learn robust features and the parameters of these paths are required to be orthogonal with each other.
End-to-end Kernel Learning via Generative Random Fourier Features
In the second-stage process, a linear learner is conducted with respect to the mapped random features.
Geometric Rényi Divergence and its Applications in Quantum Channel Capacities
We present a systematic study of the geometric R\'enyi divergence (GRD), also known as the maximal R\'enyi divergence, from the point of view of quantum information theory.
Quantum Physics Information Theory Mathematical Physics Information Theory Mathematical Physics
One-shot entanglement distillation beyond LOCC
We show in particular that the $\varepsilon$-error one-shot distillable entanglement of any pure state is the same under all sets of operations ranging from one-way LOCC to separability-preserving operations or operations preserving the set of states with positive partial transpose, and can be computed exactly as a quadratically constrained linear program.
Quantum Physics Mathematical Physics Mathematical Physics
Non-asymptotic entanglement distillation
For isotropic states, it can be further simplified to a linear program.
Quantum Physics
