The success of deep learning, a brain-inspired form AI, has sparked interest in understanding how the brain could similarly learn across multiple layers neurons. However, majority biologically-plausible learning algorithms have not yet reached performance backpropagation (BP), nor are they built on strong theoretical foundations. Here, we analyze target propagation (TP), popular but fully understood alternative to BP, from standpoint mathematical optimization. Our theory shows that TP is...

Recent advances in machine learning have significantly impacted the field of information extraction, with Large Language Models (LLMs) playing a pivotal role extracting structured from unstructured text. This paper explores challenges and limitations current methodologies entity extraction introduces novel approach to address these issues. We contribute by first introducing formalizing task Structured Entity Extraction (SEE), followed proposing Approximate Set OverlaP (AESOP) Metric designed...

Abstract It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, predictive coding theory proposed how can be learned by networks of neurons employing local synaptic plasticity. other neural sampling theories have demonstrated stochastic dynamics enable circuits represent posterior distributions latent states environment....

It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, classic predictive coding theory proposed how can be learned by networks of neurons employing local synaptic plasticity. other neural sampling theories have demonstrated stochastic dynamics enable circuits represent posterior distributions latent states environment....

While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, thorough investigation their effectiveness for processing sequential data with recurrent neural networks (RNNs) is lacking. Here, we provide the first comprehensive evaluation established CL on variety benchmarks. Specifically, shed light particularities that arise when applying weight-importance methods, such as elastic weight consolidation, RNNs. In contrast feedforward...

Equilibrium systems are a powerful way to express neural computations. As special cases, they include models of great current interest in both neuroscience and machine learning, such as deep networks, equilibrium recurrent models, or meta-learning. Here, we present new principle for learning with temporally- spatially-local rule. Our casts least-control problem, where first introduce an optimal controller lead the system towards solution state, then define reducing amount control needed...

The success of deep learning, a brain-inspired form AI, has sparked interest in understanding how the brain could similarly learn across multiple layers neurons. However, majority biologically-plausible learning algorithms have not yet reached performance backpropagation (BP), nor are they built on strong theoretical foundations. Here, we analyze target propagation (TP), popular but fully understood alternative to BP, from standpoint mathematical optimization. Our theory shows that TP is...

The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight its contribution network output. However, majority current attempts at biologically-plausible methods are either non-local time, require highly specific connectivity motives, or have no clear link any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new method that uses feedback controller drive neural...

Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. How can we achieve cooperation among self-interested, independent learning agents? Promising recent work has shown that in certain tasks be established between learning-aware agents who model the dynamics of each other. Here, present first unbiased, higher-derivative-free policy gradient algorithm reinforcement learning, which takes into account other are themselves through trial and...

The largely successful method of training neural networks is to learn their weights using some variant stochastic gradient descent (SGD). Here, we show that the solutions found by SGD can be further improved ensembling a subset in late stages learning. At end learning, obtain back single model taking spatial average weight space. To avoid incurring increased computational costs, investigate family low-dimensional late-phase models which interact multiplicatively with remaining parameters....

The success of deep learning ignited interest in whether the brain learns hierarchical representations using gradient-based learning. However, current biologically plausible methods for credit assignment neural networks need infinitesimally small feedback signals, which is problematic realistic noisy environments and at odds with experimental evidence neuroscience showing that top-down can significantly influence activity. Building upon control (DFC), a recently proposed method, we combine...

Designing reward functions for reinforcement learning is difficult: besides specifying which behavior rewarded a task, the also has to discourage undesired outcomes. Misspecified can lead unintended negative side effects, and overall unsafe behavior. To overcome this problem, recent work proposed augment specified function with an impact regularizer that discourages big on environment. Although initial results regularizers seem promising in mitigating some types of important challenges...

To make reinforcement learning more sample efficient, we need better credit assignment methods that measure an action's influence on future rewards. Building upon Hindsight Credit Assignment (HCA), introduce Counterfactual Contribution Analysis (COCOA), a new family of model-based algorithms. Our algorithms achieve precise by measuring the contribution actions obtaining subsequent rewards, quantifying counterfactual query: 'Would agent still have reached this reward if it had taken another...