arXiv subjects

Jong-Kook Kim

Publications and source records attributed to Jong-Kook Kim.

15 recordsLinked to original sources

arXiv2026

Residual Vector-based Reconstruction as Long-Context Recall Regardless of Context Window Size

Large language models (LLMs) process long contexts, including long documents and lengthy conversations, but face token-level memory usage that increases proportionally to input length. Although model optimization and lossy prompt compression are widely used, these methods still fail to solve the long-context recall problem beyond pretrained and size-constrained context windows. This paper proposes a long-context recall method that maintains near-constant GPU memory usage as context length increases, without additional training. The main idea is to reconstruct facts using parameter activations in the LLM's feed-forward layers, which store residual vectors representing facts from the source document. Utilizing residual vectors allows the LLM to deterministically reconstruct query relevant facts without referencing the original document, preserving high fidelity and reducing memory usage without fine-tuning weights. Experimental results show that the proposed method enables answering single-fact questions in two-million-token story contexts where previous methods fail.

cs.AI↗

arXiv2026

SCORE: Shape-Conforming Regions for Flight in Enclosed, Degraded Environments

Autonomous UAVs enter enclosed environments such as caves and collapsed structures that confine the vehicle and degrade perception. Conformal prediction provides a distribution-free guarantee by calibrating how far an obstacle keep-out must expand to absorb perception error at a target coverage level. However, existing keep-out regions use convex primitives whose bulges consume narrow passages and grow as perception degrades. Our main contribution defines the nonconformity score on a signed distance field (SDF). This produces a non-convex keep-out that tightly follows obstacle geometry and avoids the unnecessary bulging of equal-margin convex regions. Two supporting components keep this geometry usable as perception degrades. First, a voxelwise union of complementary sensor observations certifies voxels that any single sensor misses. Second, the margin around the obstacle adapts to measured visibility without weather labels or the online ground-truth feedback that single-pass flight cannot provide. Results on real subterranean data show that the resulting distribution-free, shape-conforming keep-out retains more usable free space than convex baselines at the same certified coverage, and produces safer closed-loop flight.

cs.RO↗

arXiv2026

P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems

Cloud-local LLM inference systems have the potential to use the reasoning capability of large cloud models while protecting sensitive user data on personal devices. Cloud-bound requests must exclude personally identifiable information (PII) to prevent external data leakage. Existing privacy-preserving methods rely on prompt perturbation, entity masking, or model fine-tuning, but these approaches may distort contextual semantics or require additional training. This paper proposes P2Skill, a prompt-based skill distillation method in which a local small language model (SLM) autonomously performs decomposition, PII-aware routing, paraphrasing, and reconstruction by following the skill prompts. Skills are iteratively refined from execution failures by a cloud LLM, enabling the local SLM to generalize beyond memorized PII patterns, and therefore P2Skill requires no privacy-specific fine-tuning or learned auxiliary detectors. Evaluation on a four-domain benchmark shows that P2Skill achieves $1.69\times$ and $3.66\times$ higher privacy-preserved inference quality than previous baselines.

cs.CR↗

arXiv2026

Region-Specific Calibration Achieves Excellent Inter-Device Reliability for Smartphone Dermatology: A Multi-Device Benchmark on Korean Facial Skin

Background: Smartphone-based dermatology requires inter-device colorimetric reliability that holds across calibration regimes, yet quantitative multi-device benchmarks remain scarce. Materials and Methods: We analyzed matched facial images from 965 Korean subjects captured by a digital single-lens reflex (DSLR) camera, a consumer tablet, and a consumer smartphone, and evaluated two calibration methods against the DSLR reference. The methods are standard global linear Color Correction Matrix (CCM) normalization and region-specific CCM trained per anatomical region, both applied in Commission Internationale de l'Eclairage Lab* (CIELAB) space. Results: Linear CCM reduced inter-device color differences by 61-74% and placed both Melanin Index (intraclass correlation coefficient [ICC] = 0.80) and Individual Typology Angle (ITA, ICC = 0.78) in the good reliability band. Region-specific CCM raised both indices into the excellent reliability band (MI ICC = 0.95, ITA ICC = 0.93), with anatomical region exceeding the source device as the largest pre-calibration variance contributor (analysis-of-variance $η^2 = 0.18$ versus 0.12). Conclusion: Consumer-device skin colorimetry therefore achieves clinically useful inter-device reliability using standard calibration, with region-aware calibration the largest remaining source of improvement.

eess.IV↗

arXiv2025

CroMe: Multimodal Fake News Detection using Cross-Modal Tri-Transformer and Metric Learning

Multimodal Fake News Detection has received increasing attention recently. Existing methods rely on independently encoded unimodal data and overlook the advantages of capturing intra-modality relationships and integrating inter-modal similarities using advanced techniques. To address these issues, Cross-Modal Tri-Transformer and Metric Learning for Multimodal Fake News Detection (CroMe) is proposed. CroMe utilizes Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models (BLIP2) as encoders to capture detailed text, image and combined image-text representations. The metric learning module employs a proxy anchor method to capture intra-modality relationships while the feature fusion module uses a Cross-Modal and Tri-Transformer for effective integration. The final fake news detector processes the fused features through a classifier to predict the authenticity of the content. Experiments on datasets show that CroMe excels in multimodal fake news detection.

cs.LG↗

arXiv2025

TT-BLIP: Enhancing Fake News Detection Using BLIP and Tri-Transformer

Detecting fake news has received a lot of attention. Many previous methods concatenate independently encoded unimodal data, ignoring the benefits of integrated multimodal information. Also, the absence of specialized feature extraction for text and images further limits these methods. This paper introduces an end-to-end model called TT-BLIP that applies the bootstrapping language-image pretraining for unified vision-language understanding and generation (BLIP) for three types of information: BERT and BLIPTxt for text, ResNet and BLIPImg for images, and bidirectional BLIP encoders for multimodal information. The Multimodal Tri-Transformer fuses tri-modal features using three types of multi-head attention mechanisms, ensuring integrated modalities for enhanced representations and improved multimodal data analysis. The experiments are performed using two fake news datasets, Weibo and Gossipcop. The results indicate TT-BLIP outperforms the state-of-the-art models.

cs.LG↗

arXiv2024

Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimation

Entropy Regularisation is a widely adopted technique that enhances policy optimisation performance and stability. A notable form of entropy regularisation is augmenting the objective with an entropy term, thereby simultaneously optimising the expected return and the entropy. This framework, known as maximum entropy reinforcement learning (MaxEnt RL), has shown theoretical and empirical successes. However, its practical application in straightforward on-policy actor-critic settings remains surprisingly underexplored. We hypothesise that this is due to the difficulty of managing the entropy reward in practice. This paper proposes a simple method of separating the entropy objective from the MaxEnt RL objective, which facilitates the implementation of MaxEnt RL in on-policy settings. Our empirical evaluations demonstrate that extending Proximal Policy Optimisation (PPO) and Trust Region Policy Optimisation (TRPO) within the MaxEnt framework improves policy optimisation performance in both MuJoCo and Procgen tasks. Additionally, our results highlight MaxEnt RL's capacity to enhance generalisation.

cs.LG↗

arXiv2024

Enabling Large Batch Size Training for DNN Models Beyond the Memory Limit While Maintaining Performance

Recent deep learning models are difficult to train using a large batch size, because commodity machines may not have enough memory to accommodate both the model and a large data batch size. The batch size is one of the hyper-parameters used in the training model, and it is dependent on and is limited by the target machine memory capacity because the batch size can only fit into the remaining memory after the model is uploaded. Moreover, the data item size is also an important factor because if each data item size is larger then the batch size that can fit into the remaining memory becomes smaller. This paper proposes a method called Micro-Batch Processing (MBP) to address this problem. This method helps deep learning models to train by providing a batch processing method that splits a batch into a size that can fit in the remaining memory and processes them sequentially. After processing the small batches individually, a loss normalization algorithm based on the gradient accumulation is used to maintain the performance. The purpose of our method is to allow deep learning models to train using larger batch sizes that exceed the memory capacity of a system without increasing the memory size or using multiple devices (GPUs).

cs.LG↗

arXiv2024

Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysis

Despite the efficient market hypothesis, many studies suggest the existence of inefficiencies in the stock market leading to the development of techniques to gain above-market returns. Systematic trading has undergone significant advances in recent decades with deep learning schemes emerging as a powerful tool for analyzing and predicting market behavior. In this paper, a method is proposed that is inspired by how professional technical analysts trade. This scheme looks at stock prices of the previous 600 days and predicts whether the stock price will rise or fall 10% or 20% within the next D days. The proposed method uses the Resnet's (a deep learning model) skip connections and logits to increase the probability of the prediction. The model was trained and tested using historical data from both the Korea and US stock markets. The backtest is done using the data from 2020 to 2022. Using the proposed method for the Korea market it gave return of 75.36% having Sharpe ratio of 1.57, which far exceeds the market return by 36% and 0.61, respectively. On the US market it gives total return of 27.17% with Sharpe ratio of 0.61, which outperforms other benchmarks such as NASDAQ, S&P500, DOW JONES index by 17.69% and 0.27, respectively.

q-fin.ST↗

arXiv2024

The Bid Picture: Auction-Inspired Multi-player Generative Adversarial Networks Training

This article proposes auction-inspired multi-player generative adversarial networks training, which mitigates the mode collapse problem of GANs. Mode collapse occurs when an over-fitted generator generates a limited range of samples, often concentrating on a small subset of the data distribution. Despite the restricted diversity of generated samples, the discriminator can still be deceived into distinguishing these samples as real samples from the actual distribution. In the absence of external standards, a model cannot recognize its failure during the training phase. We extend the two-player game of generative adversarial networks to the multi-player game. During the training, the values of each model are determined by the bids submitted by other players in an auction-like process.

cs.LG↗

arXiv2022

Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

Deep learning has recently become one of the most compute/data-intensive methods and is widely used in many research areas and businesses. One of the critical challenges of deep learning is that it has many parameters that can be adjusted, and the optimal value may need to be determined for faster operation and high accuracy. The focus of this paper is the adjustable parameters of the dataloader. The dataloader in a system mainly groups the data appropriately and loads it to the main memory for the deep learning model to use. We introduce an automated framework called Dataloader Parameter Tuner (DPT) that determines the optimal value for the parameters required for the dataloader. This framework discovers the optimal values for the number of dataloader's subprocesses (i.e., worker) and prefetch factor through grid search to accelerate the data transfer for machine learning systems.

cs.DC↗

arXiv2022

Cross-Modal Contrastive Representation Learning for Audio-to-Image Generation

Multiple modalities for certain information provide a variety of perspectives on that information, which can improve the understanding of the information. Thus, it may be crucial to generate data of different modality from the existing data to enhance the understanding. In this paper, we investigate the cross-modal audio-to-image generation problem and propose Cross-Modal Contrastive Representation Learning (CMCRL) to extract useful features from audios and use it in the generation phase. Experimental results show that CMCRL enhances quality of images generated than previous research.

cs.SD↗

arXiv2021

Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation

The emergence of quantum computing enables for researchers to apply quantum circuit on many existing studies. Utilizing quantum circuit and quantum differential programming, many research are conducted such as \textit{Quantum Machine Learning} (QML). In particular, quantum reinforcement learning is a good field to test the possibility of quantum machine learning, and a lot of research is being done. This work will introduce the concept of quantum reinforcement learning using a variational quantum circuit, and confirm its possibility through implementation and experimentation. We will first present the background knowledge and working principle of quantum reinforcement learning, and then guide the implementation method using the PennyLane library. We will also discuss the power and possibility of quantum reinforcement learning from the experimental results obtained through this work.

cs.LG↗

arXiv2021

Contextual Skipgram: Training Word Representation Using Context Information

The skip-gram (SG) model learns word representation by predicting the words surrounding a center word from unstructured text data. However, not all words in the context window contribute to the meaning of the center word. For example, less relevant words could be in the context window, hindering the SG model from learning a better quality representation. In this paper, we propose an enhanced version of the SG that leverages context information to produce word representation. The proposed model, Contextual Skip-gram, is designed to predict contextual words with both the center words and the context information. This simple idea helps to reduce the impact of irrelevant words on the training process, thus enhancing the final performance

cs.CL↗

arXiv2020

Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms

This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training.

cs.LG↗