Scaling up influence functions

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August undefined, 2024

WebOn linear models and convolutional neural networks, we demonstrate that influence functions are useful for many different purposes: to understand model behavior, debug models and detect dataset errors, and even identify and exploit vulnerabilities to adversarial training-set attacks. \icmltitlerunning WebJun 28, 2024 · Scaling Up Influence Functions Proceedings of the AAAI Conference on Artificial Intelligence We address efficient calculation of influence functions for tracking …

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WebScaling Up Inﬂuence Functions Andrea Schioppa, Polina Zablotskaia, +1 author Artem Sokolov Published 2024 Computer Science We address efﬁcient calculation of inﬂuence functions for tracking predictions back to the training data. We propose and analyze a new approach to speeding up the inverse Hes- sian calculation based on Arnoldi iteration. WebScale A translation in which the size and shape of the graph of a function is changed. Shifting and Scaling can apply on most of the functions and translate them to a new graph without loosing the properties of the old graph. The follwoing are some of common functions: Constant Function: y=c; Linear Function: y=x; Quadratic Function: y=x^2 gabapentin therapeutic category

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WebFastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging a Python library using PyTorch implementing another scalable algorithm for computing influence functions. More from the DEEL project: Xplique a Python library exclusively dedicated to explaining neural networks. WebApr 12, 2024 · A fourth way to scale up MCMC methods is to use approximate Bayesian computation (ABC), which is a family of methods that bypass the evaluation of the likelihood function by using simulations and ... WebFeb 2, 2024 · Approximating Full Conformal Prediction at Scale via Influence Functions. Javier Abad, Umang Bhatt, Adrian Weller, Giovanni Cherubin. Conformal prediction (CP) is a wrapper around traditional machine learning models, giving coverage guarantees under the sole assumption of exchangeability; in classification problems, for a chosen significance ... gabapentin therapeutic index

Shifting and Scaling - University of British Columbia

Scaling up influence functions

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WebDec 6, 2024 · Scaling Up Influence Functions Papers With Code Scaling Up Influence Functions 6 Dec 2024 · Andrea Schioppa , Polina Zablotskaia , David Vilar , Artem Sokolov … WebOct 25, 2024 · Scaling up influence functions. Andrea Schioppa; Polina Zablotskaia; Andrea Schioppa, Polina Zablotskaia, David Vilar Torres, and Artem Sokolov. 2024. Scaling up influence functions. In AAAI-22.

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WebJan 29, 2024 · Scaling up patterns from fine-scale theory and evidence may not be possible for several reasons: these include the observation that area (or volume, in aquatic … WebTo scale up inﬂuence functions to modern machine learning settings, we develop a simple, efﬁcient implementation that requires only oracle access to gradients and Hessian-vector products. We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to inﬂu-ence functions can still provide ...

WebJan 21, 2024 · Applying ideas from second-order optimization, we scale up influence functions to modern machine learning settings and show that they can be applied to high-dimensional black-box models, even in ... WebOct 3, 2024 · Variants on influence functions have also been proposed, including those using Fisher kernels (Khanna et al., 2024), tricks for faster and more scalable inference (Guo et al., 2024; Schioppa et al., 2024), and identifying relevant training samples with relative influence (Barshan et al., 2024) .

WebDec 6, 2024 · Scaling Up Influence Functions Authors: Andrea Schioppa Google Inc. Polina Zablotskaia University of British Columbia - Vancouver David Vilar Artem Sokolov We … WebTitle: Scaling Up Influence Functions; Authors: Andrea Schioppa, Polina Zablotskaia, David Vilar, Artem Sokolov; Abstract summary: We address efficient calculation of influence functions for tracking predictions back to the training data. We achieve, to the best of our knowledge, the first successful implementation of influence functions that ...

Web4 Scaling Influence Functions From the discussion above, the O(p)-memory complexity is the major bottleneck for efficient implementation of IFs. We start with an overview of …

WebNov 10, 2024 · To sum it up, you need to stop doing “keyword research” – a black box of uncertainty. Instead, find “best” and “how-to” transactional keywords that solve a problem … gabapentin therapeutic rangeWebDec 6, 2024 · Scaling Up Influence Functions. We address efficient calculation of influence functions for tracking predictions back to the training data. We propose and analyze a new approach to speeding up the inverse Hessian calculation based on Arnoldi iteration. With this improvement, we achieve, to the best of our knowledge, the first successful ... gabapentin three hundred milligramsWebSources of Leverage for Scaling. Depth:evaluation and research to understand and enhance causes of effectiveness. Sustainability:robust-design to enable adapting to negative shifts … gabapentin third trimesterWebMay 15, 2024 · No regularization + SGD: Assuming your total loss consists of a prediction loss (e.g. mean-squared error) and no regularization loss (such as L2 weight decay), then scaling the output value of the loss function by α would be equivalent to scaling the learning rate ( η) by α when using SGD: gabapentin thirstyWebApr 6, 2024 · In the Consumption and Premium plans, Azure Functions scales CPU and memory resources by adding more instances of the Functions host. The number of instances is determined on the number of events that trigger a function. Each instance of the Functions host in the Consumption plan is limited, typically to 1.5 GB of memory and … gabapentin thuocbietduocWebWe propose and analyze a new approach to speeding up the inverse Hessian calculation based on Arnoldi iteration. With this improvement, we achieve, to the best of our … gabapentin ticsWebScaling Up in Education In contrast to experiences in other sectors of society, successfully scaling up successful programs has proved very difficult in education The more complex the innovation and the greater the influence of setting, the more likely a new practice is to fail crossing the “chasm” from its gabapentin tier