「LoRA」の検索結果
86 件We study online learning with directed weakly observable feedback graphs and propose a Follow-the-Perturbed-Le
Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large pretraine
How much label noise has a network memorized, and when? We study a multilayer perceptron and what statistical
Deployed language models are updated continually, and regulators increasingly accept changes that were declare
Temporal-difference (TD) learning trains neural representations using bootstrapped targets generated by evolvi
Small students degrade on large reasoning models' (LRMs') long solutions, so recent work shortens them by inte
Frontier LLM agents can turn known vulnerabilities into working exploits, but are costly and often access-rest
How is it possible for perturbation-based methods like Evolution Strategies (ES) to optimize billion-parameter
Operating rooms (ORs) increasingly contain ambient sensing infrastructure to enable AI-based optimization of w
Low-rank adapters (LoRA) let one base model serve many fine-tuned variants: multi-tenant engines keep a single
Hyperparameter optimization (HPO) returns a single optimal configuration, systematically discarding the explor
Exemplar-free continual learning on pre-trained Vision Transformers increasingly relies on Low-Rank Adaptation
Earth system models (ESMs) rely on parameterizations to represent unresolved processes such as convection, clo
Parameter-efficient continual learning on pre-trained Vision Transformers largely relies on a costly premise:
Despite their widespread success across machine learning, the learning dynamics and generalization properties
Active-learning Kriging often replaces candidate pools by continuous metaheuristic search. A pre-specified com
Financial workflows need fast, calibrated, auditable decisions, not just generated text. We train System-1 dec
Many control tasks require driving the system into a prescribed goal set and subsequently keeping it there. Th
Overall and group-level averages, and ranking metrics such as the AUC, can hide sets of observations for which
Crime-reporting mobile applications can enhance community safety and engagement in urban areas, yet their adop
Instruction tuning for multi-task network traffic analysis requires data with clearly defined sources, supervi
Large language models (LLMs) are driving autonomous agents toward real-world environments, with the Web emergi
An experimental agent can succeed for the wrong reasons. Evaluations, however, often focus on the outcome, not
Criminal verdicts contain descriptions of criminal behavior that can support quantitative legal research, but
An agent that explores, whether it generates conjectures or research ideas, must decide which of them are wort
Career-support assistants must distinguish user claims from evidence of current ability and withhold judgments
For over 50 years, the database community has developed a wide range of successful engines to answer two funda
Even successful long CoTs may contain harmful supervision, motivating the need for methods that can automatica
When a masked diffusion language model is sampled in a few parallel steps in a random order, the model actuall
We compare how often two released language models give outdated numeric values from Norwegian statutes and reg
Looped language models can stop iterating early, and recent work shows that post-hoc readouts of the loop traj
Source-only domain generalization (DG) for remote sensing semantic segmentation seeks reliable predictions in
Geospatial text spotting involves locating and transcribing geographic labels, such as road names, district na
Multi-task dense prediction seeks a single, coherent representation that supports simultaneously discriminativ
Face anti-spoofing (FAS) models often learn attack- and dataset-specific cues that degrade performance under u
Engineering drawings specify mechanical parts through orthographic projections. Recognizing which part a drawi
Recent interactive video world models generate visually compelling videos from a single image, following user-
Reliable separation mechanisms are critical for planetary micro-rovers, which must withstand launch vibrations
Selective prediction with split conformal calibration promises a post-hoc fix for molecular property predictio
Effective multi-agent collaboration requires finalizing solutions from reasoning that is distributed across th
Electroencephalography (EEG) provides a non-invasive means for neurological and mental health assessment, yet
Harness engineering and self-evolving agents improve language-model agents quickly, yet their gains live outsi
Concept-based explanations of melanoma classifiers are rarely tested against controls with known targets. We b
Vision-language models for remote sensing answer questions about satellite images by generating text: every an
The spatial intelligence of large vision-language models (LVLMs) requires the ability to combine local visual
Robotic surface exploration benefits from tactile interfaces that accommodate contact and capture variations i
Coding agents solve repository issues through long sequences of exploration, editing, and validation. These tr
Interaction trees reuse outcomes to discover treatment-effect heterogeneity, complicating inference after grow
Across diverse markets, it is important for firms to balance attracting customers with managing operational co
LLM judges often check, against an amendment record, whether the documents an answer cites are still in force.
As learned manipulation policies become increasingly capable of solving practical manipulation tasks, assessin
In this paper, we present a reconstruction pipeline that empirically quantifies the photorealistic scene recon
Reinforcement learning (RL) is increasingly used to adapt pretrained vision-language-action models (VLAs). We
Low-rank adaptation (LoRA) enables parameter-efficient fine-tuning but can suffer from redundant basis directi
Before foraging, desert ants perform learning walks (LWs) to acquire visual memories around their nest. With e
This paper extends an existing framework for the coordination of mobile agents in unknown worlds, by consideri
Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible ge
We study first-order regret guarantees for adversarial bandits with a fixed, known directed feedback graph. Fo
In this paper, we study the regret minimization problem in episodic tabular MDPs. Our algorithm achieves the f
We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations
Many applications of multi-objective reinforcement learning (MORL) require policies that balance conflicting o
Progress toward artificial general intelligence (AGI) has largely relied on scaling a resource-intensive execu
Interactive world models generate visual content conditioned on user actions, yet maintaining spatial consiste
One-step and few-step text-to-image generation must accurately render the details specified in the prompt desp
Model distillation has become a major concern in the rapidly growing generative AI discourse. In its simplest
Occupancy-state reductions make dynamic programming possible for zero-sum partially observable stochastic game
Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as phys
Dynamic creative optimization decides which creative of an ad to render for each request. Recent work places t
Organisms must constantly weigh exploiting the resources they know against exploring for better ones. When for
The neighborhood topology of particle swarm optimization (PSO) determines information propagation and regulate
Hardware-aware neural architecture search (NAS) requires jointly optimizing predictive accuracy and device-spe
Locating relevant evidence in long inputs is a core capability of Multimodal Large Language Models. Prior stud
Probabilistic computing has proved useful for combinatorial optimization, but conventional implementations rel
Harness optimization, which improves an agent's prompts, tools, and workflows, is an increasingly effective wa
Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple
Next-generation wireless networks are expected to support high data rates and reliable connectivity while meet
Metaheuristic comparisons are often reported at evaluation budgets far larger than an expensive engineering si
Solving high-dimensional structural model updating problems requires an algorithm capable of navigating comple
Since their introduction, modern evolution strategies, such as the Covariance Matrix Adaptation Evolution Stra
Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates a
Neuromorphic hardware combines memory (synapses) and computation (neurons) into the same silicon substrate to
Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic envir
How search strategies evolve in finite, depletable landscapes remains a question in foraging theory. We study
Parameter-efficient reinforcement learning aims to improve reasoning with a compact trainable interface to a p
Population-based optimization methods often use previous search information through successful solutions, para
Adam and its variants dominate neural network training, but a single run only reveals whether a configuration