26 papers · ranked by Valyu relevance
Zachary C. Lipton, John Berkowitz, Charles Elkan
Countless learning tasks require dealing with sequential data. Image captioning, speech synthesis, and music generation all require that a model produce outputs that are sequences. In other domains, such as time series prediction, video analysis, and musical information retrieval, a model must learn from inputs that…
A. Sherstinsky
Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blogs, and implementation guides. However, in most articles, the inference formulas for the LSTM network and its parent, RNN, are stated axiomatically, while the training…
Pouria Parhami, Mansoor Fateh, Mohsen Rezvani, Hamid Alinejad Rokny
It is now well-known that genetic mutations contribute to development of tumors, in which at least 15% of cancer patients experience a causative genetic abnormality including De Novo somatic point mutations. This highlights the importance of identifying responsible mutations and the associated biomarkers (e.g., genes)…
Mehmood Ali Khan, Iftikhar Ahmed Khan, Sajid Shah, Mohammed EL-Affendi + 2 more
'Mohammed EL-Affendi' 'Waqas Jadoon' 'Mehmet Cunkas'] Background Computational intelligence (CI) based prediction models increase the efficient and effective utilization of resources for wind prediction. However, the traditional recurrent neural networks (RNN) are difficult to train on data having long-term temporal…
Shilpa Gite, Hrituja Khatavkar, Ketan Kotecha, Shilpi Srivastava + 3 more
'Priyam Maheshwari' 'Neerav Pandey' 'Rajanikanth Aluvalu'] The stock market is very complex and volatile. It is impacted by positive and negative sentiments which are based on media releases. The scope of the stock price analysis relies upon ability to recognise the stock movements. It is based on technical…
Ruben Zazo, Alicia Lozano-Diez, Javier Gonzalez-Dominguez, Doroteo T. Toledano + 2 more
'Doroteo T. Toledano' 'Joaquin Gonzalez-Rodriguez' 'Ian McLoughlin'] Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with…
Mohd Anul Haq, Ahsan Ahmed, Ilyas Khan, Jayadev Gyani + 4 more
'Abdullah Mohamed' 'El-Awady Attia' 'Pandian Mangan' 'Dinagarapandi Pandi'] The main goal of this research paper is to apply a deep neural network model for time series forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are important issues for the reliable and efficient hydrological…
Ramneet Kaur, Mudita Uppal, Deepali Gupta, Sapna Juneja + 5 more
'Syed Yasser Arafat' 'Junaid Rashid' 'Jungeun Kim' 'Roobaea Alroobaea' 'Bilal Alatas'] Cryptocurrency represents a form of asset that has arisen from the progress of financial technology, presenting significant prospects for scholarly investigations. The ability to anticipate cryptocurrency prices with extreme accuracy…
Larkin Liu, Yu-Chung Lin, Joshua Reid
Language models based on deep neural networks and traditional stochastic modelling have become both highly functional and effective in recent times. In this work, a general survey into the two types of language modelling is conducted. We investigate the effectiveness of the Hidden Markov Model (HMM), and the Long…
Shidi Liu, Yiran Wan, Wen Yang, Andi Tan + 3 more
'Paul B. Tchounwou'] Background: The novel coronavirus pneumonia that began to spread in 2019 is still raging and has placed a burden on medical systems and governments in various countries. For policymaking and medical resource decisions, a good prediction model is necessary to monitor and evaluate the trends of the…
Shailee Jain, Alexander G Huth
Language encoding models help explain language processing in the human brain by learning functions that predict brain responses from the language stimuli that elicited them. Current word embedding-based approaches treat each stimulus word independently and thus ignore the influence of context on language understanding.…
S.A. Gerasimova, A.V. Lebedeva, N.V. Gromov, A.E. Malkov + 3 more
'А.А. Fedulina' 'T.A. Levanova' 'A.N. Pisarchik'] The aim of the study is to assess the possibilities of predicting epileptiform activity using the neuronal activity data recorded from the hippocampus and medial entorhinal cortex of mice with chronic epileptiform activity. To reach this goal, a deep artificial neural…
Kazunori D Yamada
Recurrent neural networks (RNNs) are among the most promising of the many artificial intelligence techniques now under development, showing great potential for memory, interaction, and linguistic understanding. Among the more sophisticated RNNs are long short-term memory (LSTM) and gated recurrent units (GRUs), which…
Kamilya Smagulova, Kazybek Adam, Olga Krestinskaya, Alex Pappachen James
'Alex Pappachen James'] Long Short-Term memory (LSTM) architecture is a well-known approach for building recurrent neural networks (RNN) useful in sequential processing of data in application to natural language processing. The near-sensor hardware implementation of LSTM is challenged due to large parallelism and…
Julio Alberto Ramírez-Montañez, Jose de Jesús Rangel-Magdaleno, Marco Antonio Aceves-Fernández, Juan Manuel Ramos-Arreguín + 1 more
'Marco Antonio Aceves-Fernández' 'Juan Manuel Ramos-Arreguín' 'Piero Malcovati'] The present work describes the training and subsequent implementation on an FPGA board of an LSTM neural network for the modeling and prediction of the exceedances of criteria pollutants such as nitrogen dioxide (NO2), carbon monoxide…
Venkatesh Elango, Aashish N Patel, Kai J Miller, Vikash Gilja
A fundamental challenge in designing brain-computer interfaces (BCIs) is decoding behavior from time-varying neural oscillations. in typical applications, decoders are constructed for individual subjects and with limited data leading to restrictions on the types of models that can be utilized. currently, the best…
Shailee Jain, Vy A. Vo, Shivangi Mahto, Amanda LeBel + 2 more
Natural language contains information at multiple timescales. To understand how the human brain represents this information, one approach is to build encoding models that predict fMRI responses to natural language using representations extracted from neural network language models (LMs). However, these LM-derived…
Christian Bakke Vennerød, Adrian Kjærran, Erling Stray Bugge
In this paper, we introduce an LSTM cell's architecture and explain how different components go together to alter the cell's memory and predict the output. Also, the paper provides the necessary formulas and foundations to calculate a forward iteration through an LSTM. Then, the paper refers to applications and…
Sweta Kumari, C Vigneswaran, V. Srinivasa Chakravarthy
Sequential decision making tasks that require information integration over extended durations of time are challenging for several reasons including the problem of vanishing gradients, long training times and significant memory requirements. To this end we propose a neuron model fashioned after the JK flip-flops in…
Xie Chen, Sarangarajan Parthasarathy, William A. Gale, Shuangyu Chang + 1 more
'Shuangyu Chang' 'Michael Zeng'] LSTM language models (LSTM-LMs) have been proven to be powerful and yielded significant performance improvements over count based n-gram LMs in modern speech recognition systems. Due to its infinite history states and computational load, most previous studies focus on applying LSTM-LMs…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Anupiya Nugaliyadde, Kok Wai Wong, Ferdous Sohel, Hong Xie
—Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Memory Networks which contain memory are popularly used to learn patterns in sequential data. Sequential data has long sequences that hold relationships. RNN can handle long sequences but suffers from the vanishing and exploding gradient…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Angela Lopez-del Rio, Alfons Nonell-Canals, David Vidal, Alexandre Perera-Lluna
Binding prediction between targets and drug-like compounds through Deep Neural Networks have generated promising results in recent years, outperforming traditional machine learning-based methods. However, the generalization capability of these classification models is still an issue to be addressed. In this work, we…