However, existing MP-NAS methods face architectural limitations. These limitations hinder MP-NAS usage in SOT A search spaces, leaving the challenge of swiftly designing effective large models unresolved. Accuracy is the result of the network training on ImageNet for 200 epochs. An accuracy prediction model that operates without FLOPs information. Table 2 illustrates the outcomes of these models.

An operation w Model weight α The architecture parameter N The number of chunks θ The trainable parameter in the soft task-collaborative module p The parameter generated by Eq.(9) p The parameter generated by Eq.(11), replacing p during curriculum training δ The parameter to control graph structure diversity γ The parameter to control task-wise curriculum training BNRist is the abbreviation of Beijing National Research Center for Information Science and Technology. Here we provide the detailed derivation process of Eq.(10). For the other datasets, we use the task-separate head. The experiment results on OGBG datasets are shown in Table 5. From the table, our method can outperform all the multi-task NAS baselines in the three datasets.

Dense Associative Memory (DAM) models generalize the classical Hopfield model by incorporating n-body or exponential interactions that greatly enhance storage capacity. While the criticality of DAM models has been largely investigated, mainly within a statistical equilibrium picture, little attention has been devoted to the temporal self-organizing behavior induced by learning. In this work, we investigate the behavior of a stochastic exponential DAM (SEDAM) model through the lens of Temporal Complexity (TC), a framework that characterizes complex systems by intermittent transition events between order and disorder and by scale-free temporal statistics. Transition events associated with birth-death of neural avalanche structures are exploited for the TC analyses and compared with analogous transition events based on coincidence structures. We systematically explore how TC indicators depend on control parameters, i.e., noise intensity and memory load. Our results reveal that the SEDAM model exhibits regimes of complex intermittency characterized by nontrivial temporal correlations and scale-free behavior, indicating the spontaneous emergence of self-organizing dynamics. These regimes emerge in small intervals of noise intensity values, which, in agreement with the extended criticality concept, never shrink to a single critical point. Further, the noise intensity range needed to reach the critical region, where self-organizing behavior emerges, slightly decreases as the memory load increases. This study highlights the relevance of TC as a complementary framework for understanding learning and information processing in artificial and biological neural systems, revealing the link between the memory load and the self-organizing capacity of the network.

By combining Theorem 2 and Lemma 3, we can derive the proof of Corollary 4. Let There are 2 kinds of cells in the search space, including normal cells and reduction cells. After a reduction cell, the channel number is doubled. Cells are stacked sequentially to build a network. We use a set of 8 different candidate operations, including: 3 3 separable convolution; 5 5 separable convolution; 3 3 dilated separable convolution; 5 5 dilated separable convolution; 3 3 max pooling; 3 3 average pooling; identity (i.e. All the operations follow the ReLU-Conv/Pooling-BN pattern except identity and zero.

Face recognition systems are widely deployed in safety-critical applications, including law enforcement, yet they exhibit bias across a range of socio-demographic dimensions, such as gender and race. Conventional wisdom dictates that model biases arise from biased training data. As a consequence, previous works on bias mitigation largely focused on pre-processing the training data, adding penalties to prevent bias from effecting the model during training, or post-processing predictions to debias them, yet these approaches have shown limited success on hard problems such as face recognition. In our work, we discover that biases are actually inherent to neural network architectures themselves. Following this reframing, we conduct the first neural architecture search for fairness, jointly with a search for hyperparameters. Our search outputs a suite of models which Pareto-dominate all other high-performance architectures and existing bias mitigation methods in terms of accuracy and fairness, often by large margins, on the two most widely used datasets for face identification, CelebA and VGGFace2. Furthermore, these models generalize to other datasets and sensitive attributes. We release our code, models and raw data files at https://github.com/dooleys/FR-NAS.

Automatic neural architecture design has shown its potential in discovering powerful neural network architectures. Existing methods, no matter based on reinforcement learning or evolutionary algorithms (EA), conduct architecture search in a discrete space, which is highly inefficient. In this paper, we propose a simple and efficient method to automatic neural architecture design based on continuous optimization. We call this new approach neural architecture optimization (NAO). There are three key components in our proposed approach: (1) An encoder embeds/maps neural network architectures into a continuous space.

Neural architecture search methods are able to find high performance deep learning architectures with minimal effort from an expert. However, current systems focus on specific use-cases (e.g.

Cellular automata (CA) are a class of computational models that exhibit rich dynamics emerging from the local interaction of cells arranged in a regular lattice. In this work we focus on a generalised version of typical CA, called graph cellular automata (GCA), in which the lattice structure is replaced by an arbitrary graph. In particular, we extend previous work that used convolutional neural networks to learn the transition rule of conventional CA and we use graph neural networks to learn a variety of transition rules for GCA. First, we present a general-purpose architecture for learning GCA, and we show that it can represent any arbitrary GCA with finite and discrete state space. Then, we test our approach on three different tasks: 1) learning the transition rule of a GCA on a Voronoi tessellation; 2) imitating the behaviour of a group of flocking agents; 3) learning a rule that converges to a desired target state.