The Difficulty of Learning Long-Term Dependencies with Gradient Flow in Recurrent Nets

Abstract

In theory, recurrent networks (RN) can leverage their feedback connections to store activations as representations of recent input events. The most extensively used methods for learning what to put in short-term memory, on the other hand, take far too long to be practicable or do not work at all, especially when the time lags between inputs and instructor signals are long. They do not provide significant practical advantages over, the backdrop in feedforward networks with limited time windows, despite being theoretically fascinating. The goal of this article is to have a succinct overview of this rapidly evolving topic, with a focus on recent advancements. Also, we examine the asymptotic behavior of error gradients as a function of time lags to provide a hypothetical treatment of this topic. The methodology adopted in the study was to review some scholarly research papers on the subject matter to address the difficulty of learning long-term dependencies with gradient flow in recurrent nets. RNNs are the most general and powerful sequence learning algorithm currently available. Unlike Hidden Markov Models (HMMs), which have proven to be the most successful technique in a variety of sequence processing applications, they are not limited to discrete internal states and can represent continuous, dispersed sequences. As a result, they can address problems that no other method can. Conventional RNNs, on the other hand, are difficult to train due to the problem of vanishing gradients.

Including snowmelt in influent generation for cold climate WRRFs: comparison of data-driven and phenomenological approaches

“…Thanks to the LSTM memory unit, vanishing or exploding gradients, caused by the backpropagation through time in vanilla RNN training, which hampers learning of long term dependency in data sequences, can be avoided in the long-term learning process. 34,35…”

“…Thanks to the LSTM memory unit, vanishing or exploding gradients, caused by the backpropagation through time in vanilla RNN training, which hampers learning of long term dependency in data sequences, can be avoided in the long-term learning process. 34,35 The LSTM neural network has an internal memory that can learn long-term dependencies of sequential data. The LSTM unit (Fig.…”

Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting

“…RNNs have incredible success handling S2S tasks [43], where a decision at a time step t − 1 is affected by that of a time step t, signifying a temporal dependency. However, RNNs suffer from vanishing and exploding gradients and cannot capture long-term dependencies effectively [44], [45]. Gradient vanishing refers to the case where the gradient norm for long-term relationships decreases exponentially to zero, inhibiting the learning of long-term temporal relationships.…”

STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction

“…On the other hand, the chain structure design of RNNs, which strictly follow the chronological development, makes RNN models unable to predict the future and thus cannot capture the potential causal relationships [16] between traffic events. In addition, the signal of RNN must propagate along the longest long path in the network [17], [18], which leads to the more extended the way in the network, the more likely it is to lose some vital information.…”