Research
NLP
Learning Longer Memory in Recurrent Neural Networks
June 22, 2015
Abstract
Recurrent neural network is a powerful model that learns temporal patterns in sequential data. For a long time, it was believed that recurrent networks are difficult to train using simple optimizers, such as stochastic gradient descent, due to the so-called vanishing gradient problem. In this paper, we show that learning longer term patterns in real data, such as in natural language, is perfectly possible using gradient descent. This is achieved by using a slight structural modification of the simple recurrent neural network architecture. We encourage some of the hidden units to change their state slowly by making part of the recurrent weight matrix close to identity, thus forming a kind of longer term memory. We evaluate our model on language modeling tasks on benchmark datasets, where we obtain similar performance to the much more complex Long Short Term Memory (LSTM) networks (Hochreiter and Schmidhuber, 1997).
AUTHORS
Publisher
Related Publications
September 24, 2026
Reinforcement Learni9ng
MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning
Sourabh Kulkarni, Ksheeraj Sai Vepuri, Basar Demir, Jason Bohrer, Emily Shen, Jianfa Chen, Nan Jiang, Ankit Jain, Harihar Subramanyam, Mannat Singh, Chirag Nagpal
September 24, 2026
July 17, 2026
Conversational AI
Reinforcement Learni9ng
Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning
Zilin Xiao, Qi Ma, Jason Chen, Xintao Chen, Avinash Atreya, Hanjie Chen, Vicente Ordonez
July 17, 2026
June 05, 2026
Conversational AI
Ranking & Recommendations
Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval
Zeyu Yang, Qi Ma, Jason Chen, Anshumali Shrivastava
June 05, 2026
May 19, 2026
Human & Machine Intelligence
EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
Dongyan Lin, Phillip Rust, Angel Villar Corrales, Alvin W. M. Tan, Mahi Luthra, Charles-Eric Saint-James, Rashel Moritz, Sheila Krogh-Jespersen, Vanessa Stark, Surya Parimi, Jiayi Shen, Youssef Benchekroun, Yosuke Higuchi, Martin Gleize, Tom Fizycki, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Juan Pino, Michael C. Frank, Emmanuel Dupoux
May 19, 2026
October 31, 2019
NLP
Facebook AI's WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen, Jiajun Shen, Matt Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
October 31, 2019
March 14, 2019
NLP
On the Pitfalls of Measuring Emergent Communication | Facebook AI Research
Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, Yann Dauphin
March 14, 2019
January 13, 2020
NLP
Scaling up online speech recognition using ConvNets | Facebook AI Research
Vineel Pratap, Qiantong Xu, Jacob Kahn, Gilad Avidov, Tatiana Likhomanenko, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert
January 13, 2020
April 30, 2018
NLP
Computer Vision
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
April 30, 2018
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Meta © 2026
