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.
This work compares NN architectures for spectrum sensing.
BiHRNN predicts inflation by leveraging hierarchical structure and bidirectional RNNs.
Time delay neural networks (TDNNs) are an effective acoustic model for large vocabulary speech recognition. The strength of the model can be attributed to its ability to effectively model long temporal contexts. However, current TDNN models are relatively shallow, which limits the modelling capability. This paper propo…
Improved multi-modal emotion recognition using deep learning.
New method identifies valid IVs for bi-directional MR with invalid instruments.
Model captures complex neural dynamics with Gaussian process and RNN.
Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep RNN. In case of feed-forward networks training deep structures is simple and faster while learning long-term temporal information is not poss…
We present our first efforts in building an automatic speech recognition system for Somali, an under-resourced language, using 1.57 hrs of annotated speech for acoustic model training. The system is part of an ongoing effort by the United Nations (UN) to implement keyword spotting systems supporting humanitarian relief…
BICompFL tackles bi-directional compression challenges in stochastic FL, reducing communication costs by an order of magnitude.
New risk class penalizes loss deviations from mean on both sides.
DoCoFL compresses model updates for cross-device federated learning.
We discuss two parameterizations of models for marginal independencies for discrete distributions which are representable by bi-directed graph models, under the global Markov property. Such models are useful data analytic tools especially if used in combination with other graphical models. The first parameterization, i…
Applying neural-networks on Question Answering has gained increasing popularity in recent years. In this paper, I implemented a model with Bi-directional attention flow layer, connected with a Multi-layer LSTM encoder, connected with one start-index decoder and one conditioning end-index decoder. I introduce a new end-…
Bi-directional Curriculum Learning improves graph anomaly detection by considering both homogeneity and heterogeneity.
By establishing a connection between bi-directional Helmholtz machines and information theory, we propose a generalized Helmholtz machine. Theoretical and experimental results show that given \textit{shallow} architectures, the generalized model outperforms the previous ones substantially.
Delayed-RNN approximates stacked and bidirectional RNNs.
Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in …
PF-RNNs use particle filtering to model uncertainty in RNNs for better sequential data prediction.
In this paper, we explore different ways to extend a recurrent neural network (RNN) to a \textit{deep} RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find three points …
This paper studies a theoretical pruning method for RNNs to reduce computational costs.
Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthq…
Lyapunov analysis improves RNN performance prediction.
In this work, we propose a novel recurrent neural network (RNN) architecture. The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of stacking multiple recurrent layers by allowing and controlling signals flowing from upper recurrent layers to lower layers using a global gating unit for each pai…
Proposes Fusion Recurrent Neural Network for sequence data.
Recurrent neural networks (RNNs) are powerful and effective for processing sequential data. However, RNNs are usually considered "black box" models whose internal structure and learned parameters are not interpretable. In this paper, we propose an interpretable RNN based on the sequential iterative soft-thresholding al…
Efficient RNN algorithm guarantees convergence in online learning.
While Recurrent Neural Networks (RNNs) are famously known to be Turing complete, this relies on infinite precision in the states and unbounded computation time. We consider the case of RNNs with finite precision whose computation time is linear in the input length. Under these limitations, we show that different RNN va…
Paper compresses RNNs for resource-constrained devices.
RNNs struggle with in-context retrieval, while Transformers excel.
GPU-optimized ES-RNN boosts time series forecasting speed by 322x.
Paper refines RNN training by analyzing smoothness and attractors.
Novel method improves training RNNs by accelerating gradient descent.
Convolutional neural networks (CNNs) attained a good performance in hyperspectral sensing image (HSI) classification, but CNNs consider spectra as orderless vectors. Therefore, considering the spectra as sequences, recurrent neural networks (RNNs) have been applied in HSI classification, for RNNs is skilled at dealing …
Elman-type RNNs converge to globally optimal solutions in the mean-field regime.
To understand the fundamental trade-offs between training stability, temporal dynamics and architectural complexity of recurrent neural networks~(RNNs), we directly analyze RNN architectures using numerical methods of ordinary differential equations~(ODEs). We define a general family of RNNs--the ODERNNs--by relating t…
Deep neural networks have shown promising results for various clinical prediction tasks such as diagnosis, mortality prediction, predicting duration of stay in hospital, etc. However, training deep networks -- such as those based on Recurrent Neural Networks (RNNs) -- requires large labeled data, high computational res…
Recurrent Neural Networks (RNNs) have long been recognized for their potential to model complex time series. However, it remains to be determined what optimization techniques and recurrent architectures can be used to best realize this potential. The experiments presented take a deep look into Hessian free optimization…
New kernels boost RNN performance on non-time-series data.
Lyapunov exponents help understand RNN stability.
Study proves deep narrow RNNs can approximate any function, with minimum width independent of data length.
Hamiltonian RNN controls hidden states gradient for long-term dependencies.
We introduce MinimalRNN, a new recurrent neural network architecture that achieves comparable performance as the popular gated RNNs with a simplified structure. It employs minimal updates within RNN, which not only leads to efficient learning and testing but more importantly better interpretability and trainability. We…
Frequentist method estimates uncertainty in RNNs without altering architecture.
This paper proposes structurally sparse RNNs to reduce computational and memory costs.
New model handles uneven time intervals better than traditional methods.
Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time ser…
RNNs are suboptimal at compressing past sensory inputs for future prediction.
RNNs struggle with chaotic dynamics due to exploding gradients, but we found a way to optimize training.