Abstract

Extreme multi-label (XML) classification refers to the task of supervised multi-label learning that involves a large number of labels. Hence, scalability of the classifier with increasing label dimension is an important consideration. In this paper, we develop a method called LightDXML which modifies the recently developed deep learning based XML framework by using label embeddings instead of feature embedding for negative sampling and iterating cyclically through three major phases: (1) proxy training of label embeddings (2) shortlisting of labels for negative sampling and (3) final classifier training using the negative samples. Consequently, LightDXML also removes the requirement of a re-ranker module, thereby, leading to further savings on time and memory requirements. The proposed method achieves the best of both worlds: while the training time, model size and prediction times are on par or better compared to the tree-based methods, it attains much better prediction accuracy that is on par with the deep learning based methods. Moreover, the proposed approach achieves the best tail-label prediction accuracy over most state-of-the-art XML methods on some of the large datasets1 . Code: https://github.com/misterpawan/ LightDXML

Abstract

A large number of computational and scientific methods commonly require decomposing a sparse matrix into triangular factors as LU decomposition. A common problem faced during this decomposition is that even though the given matrix may be very sparse, the decompo- sition may lead to a denser triangular factors due to fill-in. A significant fill-in may lead to prohibitively larger computational costs and memory requirement during decomposition as well as during the solve phase. To this end, several heuristic sparse matrix reordering methods have been proposed to reduce fill-in before the decomposition. However, finding an optimal reordering algorithm that leads to minimal fill-in during such de- composition is known to be a NP-hard problem. A reinforcement learning based approach is proposed for this problem. The sparse matrix reorder- ing problem is formulated as a single player game. More specifically, Monte-Carlo tree search in combination with neural network is used as a decision making algorithm to search for the best move in our game. The proposed method, Alpha Elimination is found to produce significantly lesser non-zeros in the LU decomposition as compared to existing state- of-the-art heuristic algorithms with little to no increase in overall running time of the algorithm. The code for the project is publicly available 1. Keywords: Reinforcement Learning · Sparse Matrices · Deep Learning · LU · MCTS

Abstract

Recent advances in Reinforcement Learning (RL) have led to many exciting applications. These advancements have been driven by improvements in both algorithms and engineering, which have resulted in faster training of RL agents. We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents. The package is designed for training a population of agents in multi-agent environments and evaluating their ability to generalize to diverse background agents. It is built on top of DeepMind's JAX ecosystem~\cite{deepmind2020jax} and leverages the RL ecosystem developed by DeepMind. Our framework marl-jax is capable of working in cooperative and competitive, simultaneous-acting environments with multiple agents. The package offers an intuitive and user-friendly command-line interface for training a population and evaluating its generalization capabilities. In conclusion, marl-jax provides a valuable resource for researchers interested in exploring social generalization in the context of MARL. The open-source code for marl-jax is available at: \href{this https URL}{this https URL}

Abstract

A reliable critic is central to on-policy actor-critic learning. But it becomes challenging to learn a reliable critic in a multi-agent sparse reward scenario due to two factors: 1) The joint action space grows exponentially with the number of agents 2) This, combined with the reward sparseness and environment noise, leads to large sample requirements for accurate learning. We show that regularising the critic with spectral normalization (SN) enables it to learn more robustly, even in multi-agent on-policy sparse reward scenarios. Our experiments show that the regularised critic is quickly able to learn from the sparse rewarding experience in the complex SMAC and RWARE domains. These findings highlight the importance of regularisation in the critic for stable learning.

Abstract

Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation. This study investigates the use of diffusion models in generating artificial data generation for electronic circuits for enhancing the accuracy of subsequent machine learning models in tasks such as performance assessment, design, and testing when training data is usually known to be very limited. We utilize simulations in the HSPICE design environment with 22nm CMOS technology nodes to obtain representative real training data for our proposed diffusion model. Our results demonstrate the close resemblance of synthetic data using diffusion model to real data. We validate the quality of generated data, and demonstrate that data augmentation certainly effective in predictive analysis of VLSI design for digital circuits.

Abstract

Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation. This study investigates the use of diffusion models in artificial data generation for electronic circuits to enhance the accuracy of subsequent machine learning models in tasks such as performance assessment, design, and testing when training data is usually known to be very limited. We utilize simulations in the HSPICE design environment with 22nm CMOS technology nodes to obtain representative real training data for our proposed diffusion model. Our results demonstrate the close resemblance of synthetic data using diffusion models to real data. We validate the quality of generated data and demonstrate that data augmentation is certainly effective in the predictive analysis of VLSI design for digital circuits.

Abstract

We propose a novel Riemannian method called “RXML" for solving the Extreme multi-label classification problem that exploits the geometric structure of the sparse low-dimensional local embedding models. A constrained optimization problem is formulated as an optimization problem on a matrix manifold and solved using a Riemannian optimization method. A proof of convergence for the proposed Riemannian optimization method is stated. The proposed approach is tested on several real-world large scale multi-label datasets, and its usefulness is demonstrated through numerical experiments. Experimental results show that RXML improves the trade-off between train time and accuracy. At the similar level of accuracy, the train time of RXML was 1.5 to 4 times faster than that of AnnexML and EXMLDS-4, which are the state-of-the-art embedding- based methods.

Abstract

Transformers, which were introduced for solving the task of machine translation, have expanded their utility in multiple domains. A recent application of transformers is in solving elementary mathematics problems. In this paper, we use a hybrid tokenization technique for encoding the mathematics and science problems and answers, which is used to train the transformer. We compare the performance of our tokenization with that of the char-to-char tokenzation in solving various types of mathematics and science problems. We discuss the accuracy, memory usage, and time to train the model with proposed tokenization. The proposed tokenization shows higher accuracy for some problems, and requires lesser memory compared to char-to-char tokenization. We propose an extended dataset of science and mathematics problems that consists of billions of samples in questionanswer format in raw text. Code and Dataset: https: //github.com/misterpawan/scimat2

Abstract

We consider the problem of learning low-rank tensors from partial observations with structural constraints, and propose a novel factorization of such tensors, which leads to a simpler optimization problem. The resulting problem is an optimization problem on manifolds. We develop first-order and second-order Riemannian optimization algorithms to solve it. The duality gap for the resulting problem is derived, and we experimentally verify the correctness of the proposed algorithm. We demonstrate the algorithm on nonnegative constraints and Hankel constraints

Abstract

In this work, we announce a comprehensive well curated and opensource dataset with millions of samples for pre-college and college level problems in mathematics and science. A preliminary set of results using transformer architecture with character to character encoding is shown. The dataset identifies some challenging problems, and invites research on better architecture search for these problems.