query stringlengths 273 149k | pos stringlengths 18 667 | idx int64 0 1.99k | task_name stringclasses 1
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Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect. Particularly, the properties of critical points and the landscape around them are of importance to determine ... | We provide necessary and sufficient analytical forms for the critical points of the square loss functions for various neural networks, and exploit the analytical forms to characterize the landscape properties for the loss functions of these neural networks. | 0 | scitldr |
The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this “weight transport problem” , two biologically-plausible algorithms, proposed by and , relax BP’... | Biologically plausible learning algorithms, particularly sign-symmetry, work well on ImageNet | 1 | scitldr |
We introduce the 2-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors. We show that this architecture is a useful inductive bi... | We introduce the 2-simplicial Transformer and show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning. | 2 | scitldr |
We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error p... | Accurate forecasting over very long time horizons using tensor-train RNNs | 3 | scitldr |
Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret. We propose a variational message-passing algorithm for variational inference in such models. We make three contributions. First, we propose structured inference network... | We propose a variational message-passing algorithm for models that contain both the deep model and probabilistic graphical model. | 4 | scitldr |
Modern deep neural networks have a large amount of weights, which make them difficult to deploy on computation constrained devices such as mobile phones. One common approach to reduce the model size and computational cost is to use low-rank factorization to approximate a weight matrix. However, performing standard low-... | A simple modification to low-rank factorization that improves performances (in both image and language tasks) while still being compact. | 5 | scitldr |
"Deep learning training accesses vast amounts of data at high velocity, posing challenges for datase(...TRUNCATED) | "We propose a simple, general, and space-efficient data format to accelerate deep learning training (...TRUNCATED) | 6 | scitldr |
"It is fundamental and challenging to train robust and accurate Deep Neural Networks (DNNs) when sem(...TRUNCATED) | "ROBUST DISCRIMINATIVE REPRESENTATION LEARNING VIA GRADIENT RESCALING: AN EMPHASIS REGULARISATION PE(...TRUNCATED) | 7 | scitldr |
"Generative Adversarial Networks (GANs) have achieved remarkable in the task of generating realistic(...TRUNCATED) | "Are GANs successful because of adversarial training or the use of ConvNets? We show a ConvNet gener(...TRUNCATED) | 8 | scitldr |
"In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical pe(...TRUNCATED) | Equip MMD GANs with a new random-forest kernel. | 9 | scitldr |
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