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Found 23 papers, 16 papers with code
LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena
Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks.
The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions
Dataset distillation, which condenses large-scale datasets into compact synthetic representations, has emerged as a critical solution for training modern deep learning models efficiently.
KPL: Training-Free Medical Knowledge Mining of Vision-Language Models
To tackle these challenges, we introduce the Knowledge Proxy Learning (KPL) to mine knowledge from CLIP.
MedCoT: Medical Chain of Thought via Hierarchical Expert
Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical settings.
PVP: Polar Representation Boost for 3D Semantic Occupancy Prediction
Recently, polar coordinate-based representations have shown promise for 3D perceptual tasks.
Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment
Specifically, we introduce a novel method that employs dynamic and directed weight adjustment techniques to modulate the synthesis process, thereby maximizing the representativeness and diversity of each synthetic instance.
Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation
When constructing the dataset, we considered the implicit logical relationships, enabling the model to learn implicit logical relationships and dispel hallucinations.
Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models
Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling.
ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation Model
The experimental results demonstrate the powerful zero-shot and transfer learning capabilities of ViLReF, verifying the effectiveness of our pre-training strategy.
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator
This leads to inefficient utilization of the distillation budget and oversight of inter-class feature distributions, which ultimately limits the effectiveness and efficiency, as demonstrated in our analysis.
Neural Codec-based Adversarial Sample Detection for Speaker Verification
In this paper, we propose a neural codec-based adversarial sample detection method for ASV.
RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
The Vision-Language Foundation model is increasingly investigated in the fields of computer vision and natural language processing, yet its exploration in ophthalmology and broader medical applications remains limited.
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation
Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance.
EMO-SUPERB: An In-depth Look at Speech Emotion Recognition
Speech emotion recognition (SER) is a pivotal technology for human-computer interaction systems.
Deep Reinforcement Learning for Quantitative Trading
Incorporating deep reinforcement learning (DRL) with imitative learning methodologies, we bolster the proficiency of our model.
Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning
Dataset pruning aims to construct a coreset capable of achieving performance comparable to the original, full dataset.
Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation
To mitigate the adverse impact of this accumulated trajectory error, we propose a novel approach that encourages the optimization algorithm to seek a flat trajectory.
Ranked #5 on
Dataset Distillation - 1IPC
on TinyImageNet
Sharpness-Aware Training for Free
Intuitively, SAF achieves this by avoiding sudden drops in the loss in the sharp local minima throughout the trajectory of the updates of the weights.
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks
Recently, the relation between the sharpness of the loss landscape and the generalization error has been established by Foret et al. (2020), in which the Sharpness Aware Minimizer (SAM) was proposed to mitigate the degradation of the generalization.
A Research on Cross-sectional Return Dispersion and Volatility of US Stock Market during COVID-19
We also found that the epidemic has a significant negative impact on the return of the energy sector, and finally we provided our suggestions to investors.
RAIN: A Simple Approach for Robust and Accurate Image Classification Networks
However, similar to existing preprocessing-based methods, the randomized process will degrade the prediction accuracy.
On Robustness of Neural Ordinary Differential Equations
We then provide an insightful understanding of this phenomenon by exploiting a certain desirable property of the flow of a continuous-time ODE, namely that integral curves are non-intersecting.
Query-efficient Meta Attack to Deep Neural Networks
Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries.
