「lora」の検索結果
69 件Cryptocurrency markets exhibit extreme volatility and non-stationary dynamics that challenge conventional fore
Large language models (LLMs) show strong reasoning ability, but their explanations can remain inconsistent, we
Passive cooling eliminates the energy overhead and mechanical failure modes of fans, making it attractive for
Diffusion TV is an interactive AI art installation that offers a tangible and embodied experience of diffusion
Automotive infotainment validation still relies on manual testing, slow, costly, and incompatible with agile r
Improving an industrial recommender is an iterative research-and-engineering process rather than a direct path
Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy
In this paper, we study the problem of personalized survey response prediction using fine-tuned large language
Generative AI and coding agents can accelerate research software development, but they also increase the need
Assessing the impacts of social policy changes is a widely acknowledged challenge for policymakers. Econometri
Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retriev
Machine translation (MT) offers a scalable way to extend English instruction-tuning data to multiple languages
Early identification of Alzheimer's disease (AD) remains challenging because established assessment methods ca
When there is not enough labeled data to properly train deep learning models, transfer learning can help. We s
Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffus
Personalization models generate new images guided by a few subject references, while style transfer methods ai
Interactive visual world models must distinguish observation from physical intervention. Camera motion reveals
Text-guided diffusion editing raises disinformation concerns, making reliable image provenance essential. Whil
Rigid Body Dynamics (RBD) forms the computational core of real-time robotic control, but its immense computati
Merging a LoRA adapter into its base model is standard deployment practice: it removes the runtime adapter's p
We introduce the first Probably Approximately Correct (PAC) learning framework for general-sum concurrent stoc
RL-based post-training for reasoning models is increasingly bottlenecked by repeated fresh rollout generation,
Continual knowledge-updating methods are often declared superior from one final checkpoint and one conventiona
Unattended interactive autonomy - machines that step into danger in place of humans and complete tasks with hu
Designing and authoring high-performance custom kernels for accelerators is a complex task that requires deep
We evaluate three open-weight LLMs (Gemma3-12B from the USA, Bielik-11B-v3 from Poland, and Qwen3-4B from Chin
Background: Researchers increasingly use repeated identical prompts to audit stochastic variation in large lan
Retrieval-augmented generation (RAG) can improve the specificity and grounding of large language model respons
Anatomic tracer studies reveal how axon bundles project from an injection site, branch into smaller groups of
Autonomous underwater robots are widely used for exploration, monitoring, and inspection, where safe navigatio
We propose Puffin-World, a unified multimodal architecture that integrates physical understanding, spatial sim
Generative image models can now produce high-quality images, follow complex instructions, and support precise
We present OctWorld, a video diffusion framework with persistent 3D memory for generating explorable, world-co
Existing safety alignment methods for vision-language models usually modify the model behavior globally: once
Aligning video generative models to human preferences heavily relies on Reinforcement Learning (RL), which suf
Diffusion models have become the mainstream paradigm for modern visual generation and have substantially advan
Simulation is increasingly used to support safety-related decision-making in road transport, particularly for
Post-training VLA policies typically rely on supervised fine-tuning with costly expert demonstrations or reinf
Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods enable scalable Bayesian inference, but their pe
Multi-domain fine-tuning often combines MoE routing with LoRA, assuming that token-level routing separates dom
Emerging edge AI workloads increasingly require arithmetic units that can trade computational accuracy for eff
Executing long-term tasks in dynamic environments requires embodied agents to maintain robust and adaptive 3D
Combining learned policies with model predictive control can leverage learned task priors while retaining onli
Exploring unknown environments with multiple UAVs requires coordination under intermittent communication, maki
We study graph coloring with color preferences, in which each vertex ranks the available colors. In addition t
Long-tail autonomous driving failures are often framed as rare-object recognition errors. We argue that this v
Quality-diversity (QD) algorithms have been gaining traction in robot learning, where diverse motion primitive
Off-road navigation can fail when physical structures induce irrecoverable states such as high-centering or en
In animals such as elephants and octopuses, acquiring non-visual information about an object and physically en
Source term estimation (STE), which aims to estimate key properties of the gas source, is essential for identi
Long-horizon physical-world agents must reason over distant goals while grounding decisions in reliable closed
Group relative policy optimization for reinforcement learning with verifiable rewards (RLVR) typically uses a
Large language models (LLMs) increasingly interact with external environments and accumulate substantial behav
Industrial recommenders give new content initial views through budgeted exploration, then use early performanc
Searchless chess networks reach human master strength from a single forward pass by imitating a stronger teach
In this paper, we introduce ES-AHD, a novel framework that fundamentally integrates Evolution Strategy (ES) in
Developments in high-performance computing (HPC) technology continue to drastically increase quantities of ava
Large language models (LLMs) often produce fluent but incorrect answers with unwarranted confidence. A central
Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization. While lar
Population optimizers such as CMA-ES, DE, and multi-objective evolutionary algorithms drive search mainly thro
成功した突然変異戦略の中には、単一の実行で利用可能な知識が存在し、その知識は複数のタスク間で移行することができる。しかし、既存のLLM-ベースの進化的フレームワークでは、再利用可能な知識は捨てられ、同じアイディアの再発見
This note aims to serve as an entry point to the literature on learning in games, a topic with significant the
Gradient injection helps Particle Swarm Optimization (PSO) only when the swarm has identified a basin with smo
Particle swarm optimization (PSO) has been widely applied to solve complex optimization problems from real-wor
LLMが外部アクションを取り続けている場合、エージェントのメモリーが古くなったり誤った情報を持ったりする可能性があります。この問題を解決するために、この研究ではSafeCommitという技術を提案しています。SafeCo
EEG foundation-model gains may depend on cohort, montage, or probe design. We evaluated five models on five ta
Parent selection significantly affects exploration, exploitation, and complexity control in genetic programmin
ディナミカルシステムを化学的なパラフレーズで説明する手法を提案し、システムの
Constrained Optimization Problems are crucial in fields such as engineering, economics, and robotics, where hi