Arxiv:2107.03704V1 [Cs.Lg] 8 Jul 2024

Di: Amelia

However, nowadays gradually more deficiencies have been emerging for this conventional machine learning framework. On the one hand, its success largely relies on vast quantities of pre-collected annotated data, and simultaneously huge computation resources. However, most applications in real world have intrinsically rare or expensive data, or limited computation

Abstract. Domain adversarial training has shown its effective capability for find-ing domain invariant feature representations and been successfully adopted for various domain adaptation tasks. However, recent advances of large models (e.g., vision transformers) and emerging of complex adaptation scenarios (e.g., Do-mainNet) make adversarial training being easily biased Abstract Consider an ensemble of k individual classi-fiers whose accuracies are known. Upon re-ceiving a test point, each of the classifiers out-puts a predicted label and a confidence in its prediction for this particular test point. In this paper, we address the question of whether we can determine the accuracy of the ensemble. Surprisingly, even when classifiers are com-bined in Multi-view surface reconstruction is an ill-posed, inverse problem in 3D vision research. It involves modeling the geometry and appearance with appropriate surface representations. Most of the existing methods rely either on explicit meshes, using surface rendering of meshes for reconstruction, or on implicit field functions, using volume rendering of

arXiv:2107.12045v1 [cs.LG] 26 Jul 2021

arXiv:2107.04894v1 [cs.LG] 10 Jul 2021 Improving Inductive Link Prediction Using Hyper-Relational Facts Computation and Language Authors and titles for recent submissions Thu, 14 Aug 2025 Wed, 13 Aug 2025 Tue, 12 Aug 2025 Mon, 11 Aug 2025 Fri, 8 Aug 2025 See today’s new changes Abstract In any learning framework, an expert knowledge always plays a crucial role. But, in the eld of machine learning, the knowledge o ered by an expert is rarely used. Moreover, machine learning algorithms (SVM based) generally use hinge loss function which is sensitive towards the noise. Thus, in order to get the advantage from an expert knowledge and to reduce the

Abstract Zeroth-order optimization addresses problems where gradi-ent information is inaccessible or impractical to compute. While most existing methods rely on first-order approxima-tions, incorporating second-order (curvature) information can, in principle, significantly accelerate convergence. How-ever, the high cost of function evaluations required to es-timate Hessian arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.

1 Introduction Graph is a pervasive data type that represents complex relationships among entities. Graph Neural Networks (GNNs) [6, 13, 15, 44], a mainstream learning paradigm for processing type that represents complex relationships graph data, take a graph as input and learn to model the relation between nodes in the graph. GNNs have demonstrated state-of-the-art performance across various graph-related

DATA-DRIVEN REDUCED ORDER MODELING OF ENVIRONMENTAL HYDRODYNAMICS USING DEEP AUTOENCODERS AND NEURAL ODES SOURAV DUTTA1y, PETER RIVERA-CASILLAS1, ORIE M. CECIL1, MATTHEW W. FARTHING1, EMMA PERRACCHIONE2, AND MARIO PUTTI3

arXiv:2107.00844v1 [cs.LG] 2 Jul 2021

ABSTRACT Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design, where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function 1 Introduction The past few years have witnessed remarkable advances in high-dimensional statistical models, inverse problems, and learning methods for solving them. In particular, we have seen a surge of new methodologies and algorithms that have revolutionized our ability to extract insights from complex, high-dimensional data [Wainwright, 2019, Giraud,

arXiv:2107.02784v1 [cs.LG] 6 Jul 2021
arXiv.org e-Print archive
arXiv:2407.18041v1 [cs.LG] 25 Jul 2024

arXiv:2407.05005v2 [cs.LG] 18 Jul 2024 Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching

arXiv:2107.04309v1 [cs.LG] 9 Jul 2021 Understanding surrogate explanations: the interplay between complexity, delity and coverage

arXiv:2407.18745v1 [cs.LG] 26 Jul 2024 FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications

Abstract We present RELBENCH, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RELBENCH provides databases and tasks spanning diverse domains and scales, and is intended to be a founda-tional infrastructure for future research. We use RELBENCH to conduct the first comprehensive study of Relational Deep ∗. Corresponding author 1 arXiv:2107.02378v1 [cs.LG] 6 Jul 2021 Shu, Meng and Xu Table 1: Taxonomy of some typical recent literatures on meta learning based on their speci ed hyperparameters to learn. Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex are crucial for building these chatbots. However, creating effective enterprise chatbots is challenging and requires

July 8, 2021 arXiv:2107.03375v1 [cs.LG] 7 Jul 2021 Di erentiable Architecture Pruning for Transfer Learning ABSTRACT The memory and computational demands of Key-Value (KV) cache present significant challenges for deploying long-context language models. Previous approaches attempt to mitigate this issue by selectively dropping and simultaneously huge computation resources tokens, which irreversibly erases critical information that might be needed for future queries. In this paper, we propose a novel compression arXiv preprint arXiv:2009.09796, 2020. Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Ka aguchi, Gal Chechik, and Ethan Fetaya. Multi-task learning as a bargaining game.

arXiv:2107.13163v1 [cs.LG] 28 Jul 2021 Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers Many tasks are compositional and as a result, there is a combinatorial explosion of possible tasks. In this setting, when LG Artificial Intelligence exposed to a number of tasks sharing components, it is desirable for a learning system to master operations that can be reused and leveraged to generalize to entirely new tasks. Ideally, our learning systems could discover the constituent parts underlying the

Abstract Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes this particular test can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning

1 arXiv:2107.13257v1 [cs.LG] 28 Jul 2021 Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs arXiv:2311.15051v2 [cs.LG] 8 Jan 2024 Large Catapults in Momentum Gradient Descent with Warmup: An Empirical Study

ABSTRACT Conformal prediction (CP) transforms any model’s output into prediction sets guaranteed to include (cover) the true label. CP requires exchangeability, a re-laxation of the i.i.d. assumption, to obtain a valid distribution-free coverage guar-antee. This makes it directly applicable to transductive node-classification. How-ever, conventional CP cannot be applied in arXiv:2107.01705v1 [cs.LG] 4 Jul 2021 Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality⋆ arXiv:2407.20999v1 [cs.LG] 30 Jul 2024 MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning

Why BERT is so successful and how to explain? There have been some attempts to answer, but they are mainly based on experiments and observations and lack of in-depth and clear explanation. In this survey of MIT [10], they listed over 150 studies of BERT model and get the following conclusion – „BERTology has clearly come a long way, but it is fair to say we still have

arXiv:2107.00844v1 [cs.LG] 2 Jul 2021 Deep learning-based statistical noise reduction for multidimensional spectral data

Subjects: Machine Learning (cs.LG); Artificial Intelligence changes Abstract (cs.AI); Neural and Evolutionary Computing (cs.NE)

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