arXiv subjects

Taesu Kim

Publications and source records attributed to Taesu Kim.

At least 19 recordsLinked to original sources

arXiv2026

Kuranishi chart categories and higher cocycle conditions

Given an $L_\infty$-Kuranishi space introduced in \cite{Kim1}, we propose a notion called the Kuranishi chart category. Using the nerve of this category, together with a choice of atlas and a simplicial description of the covering of the underlying topological space, we formulate a higher homotopical version of the bundle-component cocycle condition. We show that this condition is always satisfied, by virtue of a property of the higher homotopy theory of $L_\infty[1]$-morphisms developed in \cite{Kim2}, concerning quasi-isomorphisms. As a consequence, the rigid cocycle condition of Fukaya-Oh-Ohta-Ono Kuranishi spaces is replaced by more flexible, homotopy-theoretic compatibility.

math.SG↗

arXiv2026

Categorical structures of Kuranishi spaces with $L_{\infty}[1]$-algebras

We introduce $L_{\infty}$-Kuranishi spaces by associating, to each chart, $L_{\infty}[1]$-algebras defined on open neighborhoods of points in the zero locus of the Kuranishi section. We show that these objects collectively form a category into which the category of smooth manifolds naturally embeds. Some notions in \cite{FOOO1} are modified to achieve the desired categorical structures; for instance, the tangent bundle condition for chart embeddings is replaced by a quasi-isomorphism condition for the $L_{\infty}[1]$-structures.

math.SG↗

arXiv2026

Homotopy models for $L_{\infty}[1]$-algebras in higher degrees

We propose a model of higher homotopy theory of $L_{\infty}[1]$-morphisms as a natural generalization of the $A_{\infty}$-homotopies defined by Fukaya-Oh-Ohta-Ono \cite{FOOO1}. Within this framework, we show that a filling condition holds for simplices whose vertices are assigned quasi-isomorphisms.

math.AT↗

arXiv2026

Stratifications associated to generic closed two-forms and stratified $L_\infty$ spaces

Jae-Suk Park and the second-named author introduce the deformation problem of coisotropic submanifolds of a symplectic manifold as the study of Mauer-Cartan moduli problem of an $L_\infty$ algebra attached to the foliation de-Rham complex associated to the null foliation of the corresponding presymplectic structure. The main purpose of the present paper is to extend this study of $L_\infty$ structures to the case of generic closed two-forms on arbitrary smooth manifolds as a stratified $L_\infty$ space. We first prove that there exists a residual subset of closed 2-forms, which we denote by $Z^2_{reg}(M) \subset Z^2(M)$, such that any element $\omega$ therefrom admits a Whitney stratification each of whose strata is a presymplectic manifold. We then associate an $L_\infty$ space to each stratum (and to its tubular neighborhood) and glue the collection of $L_\infty$ spaces to a global stratified $L_\infty$ space by the coordinate atlas consisting of $L_\infty$ morphisms, which is a collection of $L_\infty$ morphisms, not necessarily of quasi-isomorphisms.

math.SG↗

arXiv2025

$L_{\infty}$-Kuranishi spaces and the moduli space of pseudoholomorphic disks

We show that the moduli space of pseudoholomorphic disks is an example of the $L_{\infty}$-Kuranishi spaces introduced in \cite{Kim1}, provided that a condition for the existence of a stratification with a system of tubular neighborhoods holds on each chart. With respect to this structure, the forgetful and evaluation maps for the moduli space lift to morphisms between $L_{\infty}$-Kuranishi spaces.

math.SG↗

arXiv2025

GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning

Low-Rank Adaptation (LoRA) is a popular method for parameter-efficient fine-tuning (PEFT) of generative models, valued for its simplicity and effectiveness. Despite recent enhancements, LoRA still suffers from a fundamental limitation: overfitting when the bottleneck is widened. It performs best at ranks 32-64, yet its accuracy stagnates or declines at higher ranks, still falling short of full fine-tuning (FFT) performance. We identify the root cause as LoRA's structural bottleneck, which introduces gradient entanglement to the unrelated input channels and distorts gradient propagation. To address this, we introduce a novel structure, Granular Low-Rank Adaptation (GraLoRA) that partitions weight matrices into sub-blocks, each with its own low-rank adapter. With negligible computational or storage cost, GraLoRA overcomes LoRA's limitations, effectively increases the representational capacity, and more closely approximates FFT behavior. Experiments on code generation and commonsense reasoning benchmarks show that GraLoRA consistently outperforms LoRA and other baselines, achieving up to +8.5% absolute gain in Pass@1 on HumanEval+. These improvements hold across model sizes and rank settings, making GraLoRA a scalable and robust solution for PEFT. Code, data, and scripts are available at https://github.com/SqueezeBits/GraLoRA.git

cs.LG↗

arXiv2024

Debunking the CUDA Myth Towards GPU-based AI Systems

This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs, which is currently the de facto standard in AI system design. First, we create a suite of microbenchmarks to compare Intel Gaudi-2 with NVIDIA A100, showing that Gaudi-2 achieves competitive performance not only in primitive AI compute, memory, and communication operations but also in executing several important AI workloads end-to-end. We then assess Gaudi NPU's programmability by discussing several software-level optimization strategies to employ for implementing critical FBGEMM operators and vLLM, evaluating their efficiency against GPU-optimized counterparts. Results indicate that Gaudi-2 achieves energy efficiency comparable to A100, though there are notable areas for improvement in terms of software maturity. Overall, we conclude that, with effective integration into high-level AI frameworks, Gaudi NPUs could challenge NVIDIA GPU's dominance in the AI server market, though further improvements are necessary to fully compete with NVIDIA's robust software ecosystem.

cs.DC↗

arXiv2024

Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language Models

A conversational music retrieval system can help users discover music that matches their preferences through dialogue. To achieve this, a conversational music retrieval system should seamlessly engage in multi-turn conversation by 1) understanding user queries and 2) responding with natural language and retrieved music. A straightforward solution would be a data-driven approach utilizing such conversation logs. However, few datasets are available for the research and are limited in terms of volume and quality. In this paper, we present a data generation framework for rich music discovery dialogue using a large language model (LLM) and user intents, system actions, and musical attributes. This is done by i) dialogue intent analysis using grounded theory, ii) generating attribute sequences via cascading database filtering, and iii) generating utterances using large language models. By applying this framework to the Million Song dataset, we create LP-MusicDialog, a Large Language Model based Pseudo Music Dialogue dataset, containing over 288k music conversations using more than 319k music items. Our evaluation shows that the synthetic dataset is competitive with an existing, small human dialogue dataset in terms of dialogue consistency, item relevance, and naturalness. Furthermore, using the dataset, we train a conversational music retrieval model and show promising results.

cs.SD↗

arXiv2024

Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models

Binarization, which converts weight parameters to binary values, has emerged as an effective strategy to reduce the size of large language models (LLMs). However, typical binarization techniques significantly diminish linguistic effectiveness of LLMs. To address this issue, we introduce a novel binarization technique called Mixture of Scales (BinaryMoS). Unlike conventional methods, BinaryMoS employs multiple scaling experts for binary weights, dynamically merging these experts for each token to adaptively generate scaling factors. This token-adaptive approach boosts the representational power of binarized LLMs by enabling contextual adjustments to the values of binary weights. Moreover, because this adaptive process only involves the scaling factors rather than the entire weight matrix, BinaryMoS maintains compression efficiency similar to traditional static binarization methods. Our experimental results reveal that BinaryMoS surpasses conventional binarization techniques in various natural language processing tasks and even outperforms 2-bit quantization methods, all while maintaining similar model size to static binarization techniques.

cs.LG↗

arXiv2024

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). QUICK addresses the shared memory bank-conflict problem of state-of-the-art mixed precision matrix multiplication kernels. Our method interleaves the quantized weight matrices of LLMs offline to skip the shared memory write-back after the dequantization. We demonstrate up to 1.91x speedup over existing kernels of AutoAWQ on larger batches and up to 1.94x throughput gain on representative LLM models on various NVIDIA GPU devices.

cs.LG↗

arXiv2024

SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

Large language models (LLMs) have proven to be highly effective across various natural language processing tasks. However, their large number of parameters poses significant challenges for practical deployment. Pruning, a technique aimed at reducing the size and complexity of LLMs, offers a potential solution by removing redundant components from the network. Despite the promise of pruning, existing methods often struggle to achieve substantial end-to-end LLM inference speedup. In this paper, we introduce SLEB, a novel approach designed to streamline LLMs by eliminating redundant transformer blocks. We choose the transformer block as the fundamental unit for pruning, because LLMs exhibit block-level redundancy with high similarity between the outputs of neighboring blocks. This choice allows us to effectively enhance the processing speed of LLMs. Our experimental results demonstrate that SLEB outperforms previous LLM pruning methods in accelerating LLM inference while also maintaining superior perplexity and accuracy, making SLEB as a promising technique for enhancing the efficiency of LLMs. The code is available at: https://github.com/jiwonsong-dev/SLEB.

cs.CL↗

arXiv2023

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. With the active adoption of the model in various real-world applications, the need for on-device deployment has grown considerably. However, deploying large diffusion models such as Stable Diffusion with more than one billion parameters to mobile devices poses distinctive challenges due to the limited computational and memory resources, which may vary according to the device. In this paper, we present the challenges and solutions for deploying Stable Diffusion on mobile devices with TensorFlow Lite framework, which supports both iOS and Android devices. The resulting Mobile Stable Diffusion achieves the inference latency of smaller than 7 seconds for a 512x512 image generation on Android devices with mobile GPUs.

cs.LG↗

arXiv2023

OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

Large language models (LLMs) with hundreds of billions of parameters require powerful server-grade GPUs for inference, limiting their practical deployment. To address this challenge, we introduce the outlier-aware weight quantization (OWQ) method, which aims to minimize LLM's footprint through low-precision representation. OWQ prioritizes a small subset of structured weights sensitive to quantization, storing them in high-precision, while applying highly tuned quantization to the remaining dense weights. This sensitivity-aware mixed-precision scheme reduces the quantization error notably, and extensive experiments demonstrate that 3.1-bit models using OWQ perform comparably to 4-bit models optimized by OPTQ. Furthermore, OWQ incorporates a parameter-efficient fine-tuning for task-specific adaptation, called weak column tuning (WCT), enabling accurate task-specific LLM adaptation with minimal memory overhead in the optimized format. OWQ represents a notable advancement in the flexibility, efficiency, and practicality of LLM optimization literature. The source code is available at https://github.com/xvyaward/owq

cs.CL↗

arXiv2023

Cross-speaker Emotion Transfer by Manipulating Speech Style Latents

In recent years, emotional text-to-speech has shown considerable progress. However, it requires a large amount of labeled data, which is not easily accessible. Even if it is possible to acquire an emotional speech dataset, there is still a limitation in controlling emotion intensity. In this work, we propose a novel method for cross-speaker emotion transfer and manipulation using vector arithmetic in latent style space. By leveraging only a few labeled samples, we generate emotional speech from reading-style speech without losing the speaker identity. Furthermore, emotion strength is readily controllable using a scalar value, providing an intuitive way for users to manipulate speech. Experimental results show the proposed method affords superior performance in terms of expressiveness, naturalness, and controllability, preserving speaker identity.

cs.SD↗

arXiv2023

Analysis of pseudoholomorphic curves on symplectization: Revisit via contact instantons

In this survey article, we present the analysis of pseudoholomorphic curves $u:(\dot Σ,j) \to (Q \times \mathbb{R}, \widetilde J)$ on the symplectization of contact manifold $(Q,λ)$ as a subcase of the analysis of contact instantons $w:\dot Σ\to Q$, i.e., of the maps $w$ satisfying the equation $$ {\bar{\partial}}^πw = 0, \, d(w^*λ\circ j) = 0 $$ on the contact manifold $(Q,λ)$, which has been carried out by a coordinate-free covariant tensorial calculus. When the analysis is applied to that of pseudoholomorphic curves $u = (w,f)$ with $w = π_Q \circ u$, $f = s\circ u$ on symplectization, the outcome is generally stronger and more accurate than the common results on the regularity presented in the literature in that all of our a priori estimates can be written purely in terms $w$ not involving $f$. The a priori elliptic estimates for $w$ are largely consequences of various Weitzenböck-type formulae with respect to the contact triad connection introduced by Wang and the first author in [OW14], and the estimate for $f$ is a consequence thereof by simple integration of the equation $df = w^*λ\circ j$. We also derive a simple precise tensorial formulae for the linearized operator and for the asymptotic operator that admit a perturbation theory of the operators with respect to (adapted) almost complex structures: The latter has been missing in the analysis of pseudoholomorphic curves on symplectization in the existing literature.

math.SG↗

arXiv2023

Perturbation theory of asymptotic operators of contact instantons and pseudoholomorphic curves on symplectization

In this paper, we first provide precise tensorial formulae for the asymptotic operators of contact instantons $w:\dot \Sigma \to Q$ and of pseudoholomorphic curves $u:(\dot\Sigma,j) \to (Q \times \mathbb R, \widetilde J)$ on the symplectization of contact manifold $(Q,\lambda)$. The formula exhibits explicit dependence on the compatible pair $(\lambda,J)$ of the given contact triad $(Q,\lambda, J)$. Then based on this, we present a perturbation theory of the asymptotic operator under the change of compatible CR almost complex structure $J$ for given contact form $\lambda$. This perturbation theory has been missing in the literature on the study of pseudoholomorphic curves on symplectization. Through this systematic tensorial approach, we also provide the study of finer asymptotic behavior at the punctures, such as convergence of tangent plane of contact instantons, in terms of the eigenvalues and eigenvectors of the asymptotic operator, which also simplifies the corresponding study of pseudoholomorphic curves on symplectization in the literature.

math.SG↗

arXiv2022

Hi,KIA: A Speech Emotion Recognition Dataset for Wake-Up Words

Wake-up words (WUW) is a short sentence used to activate a speech recognition system to receive the user's speech input. WUW utterances include not only the lexical information for waking up the system but also non-lexical information such as speaker identity or emotion. In particular, recognizing the user's emotional state may elaborate the voice communication. However, there is few dataset where the emotional state of the WUW utterances is labeled. In this paper, we introduce Hi, KIA, a new WUW dataset which consists of 488 Korean accent emotional utterances collected from four male and four female speakers and each of utterances is labeled with four emotional states including anger, happy, sad, or neutral. We present the step-by-step procedure to build the dataset, covering scenario selection, post-processing, and human validation for label agreement. Also, we provide two classification models for WUW speech emotion recognition using the dataset. One is based on traditional hand-craft features and the other is a transfer-learning approach using a pre-trained neural network. These classification models could be used as benchmarks in further research.

cs.SD↗

arXiv2022

Affective responses to chromatic ambient light in a vehicle

This study investigates the emotional responses to the color of vehicle interior lighting using self-assessment and electroencephalography (EEG). The study was divided into two sessions: the first session investigated the potential of ambient lighting colors, and the second session was used to develop in-vehicle lighting color guidelines. Every session included thirty subjects. In the first session, four lighting colors were assessed using seventeen adjectives. As a result, 'Preference, Softness, Brightness, and Uniqueness were found to be the four factors that best characterize the atmospheric properties of interior lighting in vehicles. Ambient illumination, according to EEG data, increased people's arousal and lowered their alpha waves. The following session investigated a wider spectrum of colors using four factors extracted from the previous session. As a result, bluish and purplish lighting colors had the highest preference and uniqueness among ten lighting colors. Green received an intermediate preference and a high uniqueness score. With its great brightness and softness, Neutral White also achieved an intermediate preference rating. Despite receiving a low preference rating, warm colors were considered to be soft. Red was the least preferred color, but its uniqueness and roughness were highly rated. This study is expected to provide a basic theory on emotional lighting guidelines in the vehicle context, providing manufacturers with objective rationale.

cs.HC↗