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Found 19 papers, 7 papers with code
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity
Recent advances in text-guided image compression have shown great potential to enhance the perceptual quality of reconstructed images.
KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations
We introduce KorMedMCQA, the first Korean multiple-choice question answering (MCQA) benchmark derived from Korean healthcare professional licensing examinations, covering from the year 2012 to year 2023.
QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
We introduce QUICK, a group of novel optimized CUDA kernels for the efficient inference of quantized Large Language Models (LLMs).
Image Clustering Conditioned on Text Criteria
Classical clustering methods do not provide users with direct control of the clustering results, and the clustering results may not be consistent with the relevant criterion that a user has in mind.
Squeezing Large-Scale Diffusion Models for Mobile
The emergence of diffusion models has greatly broadened the scope of high-fidelity image synthesis, resulting in notable advancements in both practical implementation and academic research.
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
By combining these objectives, S-CLIP significantly enhances the training of CLIP using only a few image-text pairs, as demonstrated in various specialist domains, including remote sensing, fashion, scientific figures, and comics.
Prefix tuning for automated audio captioning
Audio captioning aims to generate text descriptions from environmental sounds.
Discovering and Mitigating Visual Biases through Keyword Explanation
The keyword explanation form of visual bias offers several advantages, such as a clear group naming for bias discovery and a natural extension for debiasing using these group names.
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions
Therefore, it can provide the high predictive performance and interpretability that high-stakes domains need.
Explicit Feature Interaction-aware Graph Neural Networks
However, existing graph neural networks only learn higher-order feature interactions implicitly.
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness
Randomized smoothing is currently a state-of-the-art method to construct a certifiably robust classifier from neural networks against $\ell_2$-adversarial perturbations.
Global-Local Item Embedding for Temporal Set Prediction
Temporal set prediction is becoming increasingly important as many companies employ recommender systems in their online businesses, e. g., personalized purchase prediction of shopping baskets.
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Adversarial Robustness
Randomized smoothing is currently a state-of-the-art method to construct a certifiably robust classifier from neural networks against $\ell_2$-adversarial perturbations.
One4all User Representation for Recommender Systems in E-commerce
General-purpose representation learning through large-scale pre-training has shown promising results in the various machine learning fields.
Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation
The oversmoothing of GNNs is an obstacle of GNN-based social recommendation as well.
NSML: Meet the MLaaS platform with a real-world case study
The boom of deep learning induced many industries and academies to introduce machine learning based approaches into their concern, competitively.
CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance.
NSML: A Machine Learning Platform That Enables You to Focus on Your Models
However, researchers are still required to perform a non-trivial amount of manual tasks such as GPU allocation, training status tracking, and comparison of models with different hyperparameter settings.
Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
Many-core GPU architectures show superior performance but they consume high power and also have memory constraints due to inconsistencies between cache and main memory.
