A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
DCN-V2 improves deep & cross network for web-scale learning to rank systems.
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper im…
Proposes a method to solve deep neural networks' local minimum problem.
Introduces NQ network for non-crossing quantile learning.
As a highlighting research topic in the multimedia area, cross-media retrieval aims to capture the complex correlations among multiple media types. Learning better shared representation and distance metric for multimedia data is important to boost the cross-media retrieval. Motivated by the strong ability of deep neura…
Deep neural networks enforce non-crossing quantile regression curves.
In deep neural network, the cross-entropy loss function is commonly used for classification. Minimizing cross-entropy is equivalent to maximizing likelihood under assumptions of uniform feature and class distributions. It belongs to generative training criteria which does not directly discriminate correct class from co…
Proposes a non-crossing deep neural network quantile regression method.
We identify a class of over-parameterized deep neural networks with standard activation functions and cross-entropy loss which provably have no bad local valley, in the sense that from any point in parameter space there exists a continuous path on which the cross-entropy loss is non-increasing and gets arbitrarily clos…
Hopfield networks outperform deep-learning methods in portfolio optimization.
Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessa…
This paper shows using classification instead of regression improves deep RL scalability.
Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visu…
Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are either based on matrix factorization or deep neural networks. Either of the techniques in isolation m…
Geometry-aware models improve cross-subject EEG decoding accuracy.
Proposes a deep learning model for timely and accurate recommendations.
We introduce deep scale-spaces (DSS), a generalization of convolutional neural networks, exploiting the scale symmetry structure of conventional image recognition tasks. Put plainly, the class of an image is invariant to the scale at which it is viewed. We construct scale equivariant cross-correlations based on a princ…
We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smoothin…
As the success of deep learning reaches more grounds, one would like to also envision the potential limits of deep learning. This paper gives a first set of results proving that certain deep learning algorithms fail at learning certain efficiently learnable functions. The results put forward a notion of cross-predictab…
The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfer learning is a class of algorithms underlying these techniques. In this paper, we propose a novel transfer learning approach for cross-doma…
We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks. The HSIC bottleneck is an alternative to the conventional cross-entropy loss and backpropagation that has a number of distinct advantages. It mitigates exploding and vanishing gradients, resulting in the ability…
Adam achieves optimal convergence in deep ReLU networks via novel Kakeya bounds.
Predict stock movement by considering cross effects among stocks.
This paper investigates the impact of normalization on deep neural networks for click-through rate prediction.
This paper improves image super-resolution by integrating cross-scale non-local attention.
This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed alo…
Pipeline integrates cross-sectional and longitudinal multi-omics data for IBD research.
Meta learning enables cross-domain Hamiltonian dynamics.
Researchers propose better probabilistic models for deep learning.
Given two networks with the same training loss on a dataset, when would they have drastically different test losses and errors? Better understanding of this question of generalization may improve practical applications of deep networks. In this paper we show that with cross-entropy loss it is surprisingly simple to ind…
Researchers find a class/cross-class structure in deep learning spectra.
XMixup improves transfer learning accuracy by 1.9% with less training time.
DMT enhances deep neural networks to better preserve data structures.
In recent years, there have been numerous developments towards solving multimodal tasks, aiming to learn a stronger representation than through a single modality. Certain aspects of the data can be particularly useful in this case - for example, correlations in the space or time domain across modalities - but should be…
Deep CNNs diagnose chest X-rays for COVID-19 and other pneumonia.
DHEN improves CVR prediction for ads with multitask learning and auxiliary loss.
In this paper, we propose a novel structure for a cross-modal data association, which is inspired by the recent research on the associative learning structure of the brain. We formulate the cross-modal association in Bayesian inference framework realized by a deep neural network with multiple variational auto-encoders …
Survey of multimodal deep generative models for diverse data types.
FuncNN package enables deep learning with functional covariates.
Deep learning detects APT attacks with high accuracy and low false positives.
This paper uses deep learning to detect money laundering in cross-border transactions.
Optimizes tensor rank selection for neural network compression.
Deep learning predicts drug prescriptions across global health records.
MetFA aligns source and target domains for cross-device image classification.
Model compression has been widely adopted to obtain light-weighted deep neural networks. Most prevalent methods, however, require fine-tuning with sufficient training data to ensure accuracy, which could be challenged by privacy and security issues. As a compromise between privacy and performance, in this paper we inve…
EIDGM model estimates DE parameters from RCS data.
CAggNet improves medical image segmentation by fusing coarse and fine features.
DNN2LR bridges DNN power and LR interpretability.