A Metalearned Neural Circuit for Nonparametric Bayesian Inference
- URL: http://arxiv.org/abs/2311.14601v1
- Date: Fri, 24 Nov 2023 16:43:17 GMT
- Title: A Metalearned Neural Circuit for Nonparametric Bayesian Inference
- Authors: Jake C. Snell, Gianluca Bencomo, Thomas L. Griffiths
- Abstract summary: Most applications of machine learning to classification assume a closed set of balanced classes.
This is at odds with the real world, where class occurrence statistics often follow a long-tailed power-law distribution.
We present a method for extracting the inductive bias from a nonparametric Bayesian model and transferring it to an artificial neural network.
- Score: 4.767884267554628
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most applications of machine learning to classification assume a closed set
of balanced classes. This is at odds with the real world, where class
occurrence statistics often follow a long-tailed power-law distribution and it
is unlikely that all classes are seen in a single sample. Nonparametric
Bayesian models naturally capture this phenomenon, but have significant
practical barriers to widespread adoption, namely implementation complexity and
computational inefficiency. To address this, we present a method for extracting
the inductive bias from a nonparametric Bayesian model and transferring it to
an artificial neural network. By simulating data with a nonparametric Bayesian
prior, we can metalearn a sequence model that performs inference over an
unlimited set of classes. After training, this "neural circuit" has distilled
the corresponding inductive bias and can successfully perform sequential
inference over an open set of classes. Our experimental results show that the
metalearned neural circuit achieves comparable or better performance than
particle filter-based methods for inference in these models while being faster
and simpler to use than methods that explicitly incorporate Bayesian
nonparametric inference.
Related papers
- Scaling and renormalization in high-dimensional regression [72.59731158970894]
This paper presents a succinct derivation of the training and generalization performance of a variety of high-dimensional ridge regression models.
We provide an introduction and review of recent results on these topics, aimed at readers with backgrounds in physics and deep learning.
arXiv Detail & Related papers (2024-05-01T15:59:00Z) - Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple
Logits Retargeting Approach [102.0769560460338]
We develop a simple logits approach (LORT) without the requirement of prior knowledge of the number of samples per class.
Our method achieves state-of-the-art performance on various imbalanced datasets, including CIFAR100-LT, ImageNet-LT, and iNaturalist 2018.
arXiv Detail & Related papers (2024-03-01T03:27:08Z) - On the Dynamics of Inference and Learning [0.0]
We present a treatment of this Bayesian updating process as a continuous dynamical system.
We show that when the Cram'er-Rao bound is saturated the learning rate is governed by a simple $1/T$ power-law.
arXiv Detail & Related papers (2022-04-19T18:04:36Z) - Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
There is an increasing need for active learning algorithms that are compatible with deep neural networks.
This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
arXiv Detail & Related papers (2021-06-17T17:26:31Z) - The Gaussian equivalence of generative models for learning with shallow
neural networks [30.47878306277163]
We study the performance of neural networks trained on data drawn from pre-trained generative models.
We provide three strands of rigorous, analytical and numerical evidence corroborating this equivalence.
These results open a viable path to the theoretical study of machine learning models with realistic data.
arXiv Detail & Related papers (2020-06-25T21:20:09Z) - Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers.
We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model.
Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
arXiv Detail & Related papers (2020-06-22T21:12:31Z) - Automatic Recall Machines: Internal Replay, Continual Learning and the
Brain [104.38824285741248]
Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity.
We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective.
Instead the implicit memory of learned samples within the assessed model itself is exploited.
arXiv Detail & Related papers (2020-06-22T15:07:06Z) - Mean-Field Approximation to Gaussian-Softmax Integral with Application
to Uncertainty Estimation [23.38076756988258]
We propose a new single-model based approach to quantify uncertainty in deep neural networks.
We use a mean-field approximation formula to compute an analytically intractable integral.
Empirically, the proposed approach performs competitively when compared to state-of-the-art methods.
arXiv Detail & Related papers (2020-06-13T07:32:38Z) - What needles do sparse neural networks find in nonlinear haystacks [0.0]
A sparsity inducing penalty in artificial neural networks (ANNs) avoids over-fitting, especially in situations where noise is high and the training set is small.
For linear models, such an approach provably also recovers the important features with high probability in regimes for a well-chosen penalty parameter.
We perform a set of comprehensive Monte Carlo simulations on a simple model, and the numerical results show the effectiveness of the proposed approach.
arXiv Detail & Related papers (2020-06-07T04:46:55Z) - Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks [78.76880041670904]
In neural networks with binary activations and or binary weights the training by gradient descent is complicated.
We propose a new method for this estimation problem combining sampling and analytic approximation steps.
We experimentally show higher accuracy in gradient estimation and demonstrate a more stable and better performing training in deep convolutional models.
arXiv Detail & Related papers (2020-06-04T21:51:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.