Derives a biologically plausible neural network for Slow Feature Analysis.
problem Learning latent features from time series data.
method Starting from an SFA objective, derives Bio-SFA with a biologically plausible neural network implementation.
result Validates Bio-SFA on naturalistic stimuli, reproducing interesting properties of brain cells.
New method generates plausible counterfactuals for time series classification.
problem Generating realistic counterfactuals for time series data.
method Gradient-based optimization with soft-DTW alignment and multi-faceted loss function.
result Our method outperforms existing approaches in temporal realism and distributional alignment.
OFVF uses Bayesian plausibility sets for robust exploration in reinforcement learning.
problem Exploration in reinforcement learning, especially in poorly understood states and actions.
method Bayesian approach with plausibility sets constructed based on sensible value functions.
result OFVF constructs tighter optimistic estimates for exploration, leading to robust policies.
Paper introduces 'plausible deniability' for privacy-preserving data synthesis.
problem Challenges in releasing full data records while preserving privacy.
method Introduces 'plausible deniability' criterion and mechanisms for generating synthetic datasets.
result Generative technique preserves utility of original data and is efficient for large datasets.
Biological neural network mimics CCA for multi-channel data.
problem Implementing CCA in a biologically plausible neural network.
method Derive an online CCA algorithm with local synaptic updates for multi-compartmental neurons.
result The derived neural network architecture and synaptic updates resemble cortical pyramidal neuron behavior.
ARFs generate plausible counterfactuals for models, improving model understanding.
problem Creating realistic counterfactuals for model analysis.
method Adversarial Random Forests (ARFs) for generating plausible counterfactuals.
result ARFs efficiently generate plausible counterfactuals in a model-agnostic way.
New algorithm shows neural networks can learn without full backpropagation.
problem Stochastic gradient descent with backpropagation is non-biologically plausible.
method Random and fixed backpropagation weights in a feedback alignment algorithm.
result Error converges to zero exponentially fast in overparameterized networks.
Extends image-to-image translation to multiple distributions, allowing composite functions.
problem Limited to single pair translations, new mechanism scalable to multiple distributions.
method Decoupled training mechanism for multiple distributions, composite translation functions.
result Generates images with characteristics not seen in training set.
We define and compute plausible counterfactual explanations using density constraints.
problem Efficiently compute plausible counterfactual explanations for machine learning models.
method Propose and study a formal definition of plausible counterfactual explanations, use density estimators, and introduce convex density constraints.
result Convex density constraints ensure plausible and feasible counterfactual explanations.
We propose a method to model multi-agent behaviors with limited observation and mechanical constraints.
problem Modeling real-world multi-agent behaviors with limited observation and mechanical constraints.
method Decentralized generative models with partial observation and mechanical constraints based on hierarchical variational recurrent neural networks.
result Our method effectively models and predicts biologically plausible behaviors with minimal constraint violations.
Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost…
Paper explores BP's biological plausibility and alternative learning algorithms.
problem Backpropagation's biological plausibility in neural networks.
method Framing supervised learning in the Lagrangian framework to devise biologically plausible local algorithms.
result Biologically plausible learning algorithms can be devised based on saddle point search in the learning adjoint space.
Method generates plausible financial stress scenarios using large deviations.
problem Misleading risk management by overlooking or overemphasizing implausible scenarios.
method Exploits large-deviations principle to concentrate risk factors near most likely stress configurations.
result Can generate informative stress scenarios even with limited historical data.
AR algorithm simplifies backpropagation with improved scalability and biological plausibility.
problem Improving backpropagation algorithms for complex neural networks and biological plausibility.
method Introducing learnable backwards weights and avoiding nonlinear derivative computations; relaxing frozen feedforward pass assumption.
result Simplified AR algorithm maintains performance on complex CNN architectures and challenging datasets.
Generative model learns distribution over plausible segmentations.
problem Learning from ambiguous images in clinical applications.
method Probabilistic U-Net combining U-Net and conditional variational autoencoder.
result Significantly better at reproducing plausible segmentations than previous methods.
Silence on suspicious stock market patterns persists despite lack of plausible explanations.
problem Suspicious patterns in stock market returns not explained or warned about.
method Analysis of correspondence and market data over five years.
result People aware of suspicious patterns chose not to alert the public.
QSD enhances deep network performance through biologically plausible dropout.
problem Overfitting in deep networks.
method Quantal Synaptic Dilution (QSD) model based on neuronal synapses.
result QSD outperforms standard dropout in various deep network architectures.
New neural network theory mimics physics laws, making computations more plausible.
problem Neural networks lack biological plausibility in computations.
method Variational framework of the least action principle, local in space and time.
result SpatioTemporal Local Propagation (STLP) scheme is biologically plausible.
New framework predicts diverse, contextually plausible 3D human motions.
problem Predicting multiple plausible future 3D poses given observed poses.
method Developed a new variational framework that conditions latent variable on past observation to encourage relevant information.
result Our approach generates motions of higher quality and preserves contextual information.
The paper explores metrics and models for analyzing biological shapes.
problem Analyzing biological shapes using mathematical metrics.
method Review of Riemannian metrics and evolution equations, focusing on diffeomorphic shape analysis.
result Introduction of a new class of metrics involving optimization of a growth tensor.
GAIT-prop derives a biologically plausible learning rule from backpropagation.
problem Biological implausibility in traditional backpropagation for neural networks.
method GAIT-prop uses a top-down model to convert output error into plausible targets for weight updates.
result GAIT-prop and backpropagation give identical weight updates under certain conditions.
Two local learning rules are investigated to avoid weight transport in neural networks.
problem Local learning rules that avoid weight transport are unstable and require tuning.
method Investigated two non-local learning rules and a more robust local rule.
result Non-local learning rules match state-of-the-art performance and operate effectively in noisy updates.
Shallow networks with local learning rules can match deep learning performance.
problem Training deep neural networks is biologically implausible; the goal is to achieve similar performance with shallow networks.
method Investigated shallow networks with one hidden layer and a single readout layer, using various local learning rules for the hidden layer and supervised learning for the readout layer.
result Shallow networks can achieve test accuracy comparable to deep learning models, suggesting the use of different datasets for testing.
Biologically plausible learning algorithms can match BP on large datasets.
problem Learning algorithms that are biologically plausible often perform poorly on large datasets.
method Evaluation of sign-symmetry and feedback alignment algorithms on ImageNet and MS COCO.
result Sign-symmetry algorithm can match BP performance on ImageNet and MS COCO.
Backpropagation is explained as a diffusion process in neural networks.
problem The biological plausibility of Backpropagation is questioned.
method Demonstrated that time-delayed neurons and forward-backward waves approximate the gradient in deep networks.
result Backpropagation can be interpreted as a diffusion process, approximating the gradient for non-fast inputs.
New learning rules from information bottleneck improve deep learning without precise labels.
problem Training deep neural networks with backpropagation is biologically implausible.
method Kernelized information bottleneck principle with 3-factor Hebbian structure.
result The new learning rules perform nearly as well as backpropagation on image classification tasks.
New measures for prediction validity and consonant plausibility introduced.
problem Challenges in predicting future observations and quantifying prediction uncertainty.
method Introducing Type-2 validity and using consonant plausibility measures and conformal prediction.
result Achieving both Type-1 and Type-2 validity through consonant plausibility measures and conformal prediction.
Two approaches to model selection in networks yield similar results, but not always.
problem Determining which model best describes a network while avoiding overfitting.
method Comparing model selection based on posterior probability and predictive performance.
result The most plausible model is not always the most predictive, leading to potential overfitting.
New method isolates epistemic uncertainty in diffusion models, improving plausibility scores.
problem Uncertainty quantification in diffusion models, especially epistemic uncertainty.
method Fisher information based approach using FLARE (Fisher-Laplace Randomized Estimator).
result FLARE improves uncertainty estimation in synthetic time-series generation tasks.
Chebyshev steps improve convergence in deep-unfolded gradient descent.
problem Improving convergence speed in iterative algorithms.
method Introducing Chebyshev steps to bound convergence rate of gradient descent.
result Chebyshev steps lead to asymptotically optimal convergence rate.
Proposes a new neural network approach to credit assignment.
problem Credit assignment problem in deep neural networks.
method Contrastive similarity matching objective function.
result Deep networks learn to match similarity between layers.
New algorithm improves efficiency of Bayesian Causal Forest for subgroup analysis.
problem Estimating heterogeneous effects in subgroup analysis.
method Developed a novel algorithm for fitting Bayesian Causal Forest (BCF) model, more efficient than Gibbs sampler.
result New algorithm improves posterior exploration and coverage of interval estimates.
Enhances patient failure prediction using dynamic survival models.
problem Lack of precise individual level prediction in conventional models.
method Developed counterfactual dynamic survival model (CDSM).
result Inflection point of estimated survival curves predicts patient failure time.
This note improves correlation stress tests using geodesic distance.
problem Improving financial risk management through better covariance stress tests.
method Proposes a new geometrically invariant definition of correlation stress tests.
result Demonstrates a submanifold approach to stress testing covariance matrices.
We propose to interpret distribution model risk as sensitivity of expected loss to changes in the risk factor distribution, and to measure the distribution model risk of a portfolio by the maximum expected loss over a set of plausible distributions defined in terms of some divergence from an estimated distribution. The…
A common assumption in financial engineering is that the market price for any derivative coincides with an objectively defined risk-neutral price - a plausible assumption only if traders collectively possess objective knowledge about the price dynamics of the underlying security over short time scales. Here we assume t…
One conjecture in both deep learning and classical connectionist viewpoint is that the biological brain implements certain kinds of deep networks as its back-end. However, to our knowledge, a detailed correspondence has not yet been set up, which is important if we want to bridge between neuroscience and machine learni…
Researchers disrupt Gaussian model inference to test adversarial attacks.
problem Disrupting conditional inference in multivariate Gaussian models under adversarial conditions.
method Considered white- and grey-box settings with complete and incomplete knowledge of the Gaussian distribution, respectively. Reduced to quadratic and stochastic quadratic programs. Derived structural properties for solution methods.
result Demonstrated the impact and efficacy of attacks in various applications, including real estate evaluation, interest rate estimation, and signals processing.
New learning algorithm mimics biological neural networks.
problem Biologically implausible backpropagation for directed neural networks.
method Introduces new neuronal dynamics and learning rule for arbitrary architectures, sparsity-inducing pruning method, and dynamical-systems characterization.
result Prunes irrelevant connections and improves learning efficiency.
Proposes TNCM-VAE for generating causal financial time series.
problem Lack of causal reasoning in market generators.
method Combines VAE with structural causal models, enforcing causal constraints through DAGs and using causal Wasserstein distance.
result Superior performance in counterfactual probability estimation, L1 distances as low as 0.03-0.10.
New method uses entropy to generate multiple plausible causal maps.
problem Learning causal relationships from noisy data can lead to artifacts in DAGs.
method Entropy-based inference to generate an ensemble of plausible causal graphs.
result Multiple causal maps consistent with underlying data variability.
Stock markets show unusual overnight and intraday returns.
problem Unusual patterns of overnight and intraday returns in stock markets.
method Analyzed features of the returns to deduce the cause.
result The only plausible explanation for these returns is that they are due to market manipulation.
Unsupervised learning by hidden units with biological plausibility.
problem Training neural networks without labeled data.
method Global inhibition in hidden layer to learn feature detectors.
result Learned feature detectors enable supervised training of higher layers.
In this paper we perform a statistical analysis of the high-frequency returns of the IBEX35 Madrid stock exchange index. We find that its probability distribution seems to be stable over different time scales, a stylized fact observed in many different financial time series. However, an in-depth analysis of the data us…
New method trains neural networks with local error signals, outperforming global methods.
problem Training neural networks with global error signals.
method Layer-wise training with local error signals.
result Layer-wise training with local error signals can approach state-of-the-art performance.
The paper relaxes constraints on predictive coding models, making them more biologically plausible.
problem Neurophysiological models of predictive coding are not fully biologically plausible.
method The paper relaxes constraints on standard predictive coding algorithms by removing neurally implausible features.
result The removal of neurally implausible features does not significantly affect learning performance.
This work combines RBMs with neural nets for reliable source and statement estimation.
problem Discovering the truth from conflicting or scarce claims.
method Combines RBMs with neural nets for unsupervised inference.
result Significantly outperforms state-of-the-art methods.
New method uses VAEs to generate financial correlation matrices for credit portfolio VaR analysis.
problem Quantifying credit portfolio sensitivity to asset correlations.
method Employing Variational Autoencoders (VAEs) to generate synthetic financial correlation matrices.
result The VAE latent space captures crucial factors impacting portfolio diversification, especially in credit portfolio sensitivity to asset correlations.