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.
Graph neural network constructs a sparse latent point cloud from dense point clouds.
Encoder-decoder networks using convolutional neural network (CNN) architecture have been extensively used in deep learning literatures thanks to its excellent performance for various inverse problems. However, it is still difficult to obtain coherent geometric view why such an architecture gives the desired performance…
Convolutional auto-encoders learn natural exponential family distributions.
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We …
Performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Many image classification tasks, such as those related to cellular imaging, exhibit invariance to rotation. We present a novel scheme using the magnitude response of the 2D-discrete-Fourier transform (2D-DFT)…
Image denoising is always a challenging task in the field of computer vision and image processing. In this paper, we have proposed an encoder-decoder model with direct attention, which is capable of denoising and reconstruct highly corrupted images. Our model consists of an encoder and a decoder, where the encoder is a…
Convolutional network converts speaker voices without text.
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-d…
CBC makes CNNs robust against adversarial attacks with minimal computational overhead.
Paper introduces Laplace-HDC for better binary hyperdimensional computing.
We study the use of a time series encoder to learn representations that are useful on data set types with which it has not been trained on. The encoder is formed of a convolutional neural network whose temporal output is summarized by a convolutional attention mechanism. This way, we obtain a compact, fixed-length repr…
Adaptive graph auto-encoder improves general data clustering.
Paper proposes a faster RAE with sequence-aware encoding.
Improved anomaly detection in time series data using kervolutional neural networks.
Convolutional neural networks (CNN) have recently achieved state-of-the-art results in various applications. In the case of image recognition, an ideal model has to learn independently of the training data, both local dependencies between the three components (R,G,B) of a pixel, and the global relations describing edge…
Given a convolutional dictionary underlying a set of observed signals, can a carefully designed auto-encoder recover the dictionary in the presence of noise? We introduce an auto-encoder architecture, termed constrained recurrent sparse auto-encoder (CRsAE), that answers this question in the affirmative. Given an input…
CRRN detects printer defects in SPI data of SMT boards.
Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates…
Two graph auto-encoders decouple feature propagation from graph convolution layers.
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervised and unsupervised. The effects on training stability of different objective functions are compared. We add an encoder to the network, makin…
Novel model predicts anticancer compound sensitivity with high accuracy and interpretability.
Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multivariate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, how…
In "extreme" computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging. Incorporating image mapping convolutional neural networks (CNN) into iterative image recovery has great potential to resolve this issue. This pape…
Improves energy efficiency of neuromorphic hardware by optimizing memory organization and encoding schemes.
DGA and DVGA learn disentangled graph representations to improve graph analysis.
Two-step process generates molecules from latent vectors.
Paper uses CNNs for eye tracking data segmentation, generation, and reconstruction.
New model encodes multivariate signals more efficiently with sparsity and low-rank constraints.
Revisited Lmser for image recognition with convolutional layers.
Graph Convolutional Networks improve prosthetic sensation interpretation.
A novel approach predicts long-term stock price trends using 2D-convolutional encoders and semantic segmentation.
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions.…
AGE improves graph embedding by smoothing features and iteratively enhancing node embeddings.
AEGCN uses autoencoder constraints to improve graph node classification.
We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model us…
New method uses neural networks to interpolate stellar atmospheres with high precision.
It has recently been observed that certain extremely simple feature encoding techniques are able to achieve state of the art performance on several standard image classification benchmarks including deep belief networks, convolutional nets, factored RBMs, mcRBMs, convolutional RBMs, sparse autoencoders and several othe…
RNA-binding proteins (RBPs) play crucial roles in many biological processes, e.g. gene regulation. Computational identification of RBP binding sites on RNAs are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence motifs. Thus, fast locating those motifs on RNA sequences is crucial and time-efficie…
This paper tackles spatio-temporal information preservation in machine learning.
STAR-GCN improves recommender systems by learning node representations.
BinConv improves time series forecasting by preserving ordinal information in a classification framework.
Convolution and pooling improve kernel methods in image classification.
Paper tackles zero-shot activity recognition using video features and text embeddings.
This paper explores the capabilities of convolutional neural networks to deal with a task that is easily manageable for humans: perceiving 3D pose of a human body from varying angles. However, in our approach, we are restricted to using a monocular vision system. For this purpose, we apply a convolutional neural networ…
TaLK Convolutions improve sequence modeling efficiency.
New CSC model extracts EEG signals with low noise sensitivity.
Stochastic encoding improves gender classification of brain networks from UK Biobank data.
Study finds CNNs perform better with financial ratio data than fundamental data.