New model DWM mimics human memory, learning to retain, ignore or forget.
problem Lack of effective separation between episodic and working memory in neural networks.
method Designed Differentiable Working Memory (DWM) inspired by psychological studies.
result DWM learns psychology-inspired tasks faster and generalizes to longer sequences.
MAES architecture improves working memory task performance through multi-task and transfer learning.
problem Improving performance on complex working memory tasks.
method Memory-Augmented Encoder-Solver (MAES) architecture with dual recurrent neural network controllers and a shared memory module.
result MAES models achieve task-size generalization, handling inputs 50 times longer than training data.
Model combines long-term and short-term memory using conceptors.
problem Transfer between long-term and short-term memory.
method Recurrent neural network with gated reservoir for short-term memory and conceptors for long-term memory.
result Standard operations on conceptors allow combining long-term memories and describing their effect on short-term memory.
Eye tracking measures ADHD-related working memory deficits.
problem Diagnosing ADHD in adults requires reliable measures of working memory capacity.
method Eye tracking technology and machine learning applied to a working memory task.
result Machine learning generated features unique to ADHD.
Active-memory mechanisms can replace self-attention in Transformers, but optimal results often require both.
problem Replacing self-attention with active-memory mechanisms in Transformers.
method Evaluation of various active-memory mechanisms in a Transformer model.
result Active-memory mechanisms can achieve comparable results to self-attention for language modeling, but optimal results are often achieved by combining both mechanisms.
A new model for sequential memory using temporal predictive coding.
problem Forming accurate memory of sequential stimuli in the brain.
method Proposes a novel PC-based model called temporal predictive coding (tPC).
result Shows that tPC models can accurately memorize and retrieve sequential inputs.
This work analyzes and optimizes memory and compute costs of learned optimizers.
problem High memory and compute costs of learned optimizers.
method Identified and quantified design features of learned and hand-designed optimizers, constructed a more efficient learned optimizer.
result A learned optimizer that is faster and more memory efficient than previous work.
Low-rank training improves neural network training on edge devices with non-volatile memory.
problem Training neural networks on edge devices with non-volatile memory, especially in terms of write density and auxiliary memory.
method Low-rank training scheme to address write density and auxiliary memory limitations.
result The low-rank training technique outperforms standard SGD in accuracy and weight writes.
This work tackles online memory selection in continual learning using information theory.
problem Online selection of a representative replay memory from data streams.
method Information-theoretic criteria (surprise, learnability) and Bayesian model for efficient computation.
result InfoRS improves robustness against data imbalance compared to reservoir sampling.
A new memory model enhances deep learning's visual understanding.
problem Lack of short-term memory in deep learning models.
method Introduces a biologically inspired visual working memory architecture.
result Model achieves competitive classification performance and reconstructs images.
Memory-limited learning tackles adversarial bandits with reduced storage.
problem Adversarial bandit problem with limited memory storage.
method Hierarchical learning policy with sublinear memory requirement.
result Established sublinear regret bounds for weak and shifting regrets.
This study examines memory effects in S&P500 market correlations using Langevin models.
problem The neglect of memory effects in market correlations for optimal portfolio selection.
method Fit a generalised Langevin equation (GLE) to S&P500 market correlation data.
result Memory effects in market correlations significantly improve forecasting accuracy and suggest a hidden slow time scale.
This paper improves RNN memory capacity for long sequences through learning associative memory update rules.
problem Challenges in RNNs remembering long sequences.
method Jointly learns memory update rule with task objective and uses multiple associative memories.
result Improves memory capacity for long sequence encoding.
EAST compresses deep ConvNets for tiny memory nodes.
problem Memory constraints in tiny devices for deep ConvNets.
method Encoding-Aware Sparse Training (EAST) with adaptive group pruning and LZ4 weight encoding.
result EAST achieves deep memory compression with lower sparsity and higher accuracy.
New memory allocation scheme improves image generation performance.
problem Improving episodic and semantic memory representation in neural networks.
method Developed a hierarchical latent variable model with differentiable, locally block allocated latent memory.
result Improved conditional likelihood values on various datasets.
Memory Matching Networks classify DNA sequences for protein binding sites.
problem Manual construction of DNA motifs is difficult due to their complexity.
method Memory Matching Networks (MMN) learn a dynamic memory bank of encoded motifs and match them to new sequences.
result MMN effectively classifies DNA sequences as protein binding or nonbinding sites.
New model forecasts long-memory series with time-varying parameters.
problem Forecasting long-memory series with dynamic parameters.
method Proposes a new long-memory model with a time-varying fractional parameter, driven by predictive likelihood score.
result Validated through Monte Carlo experiment and real data applications.
Improved EXACT strategy reduces GNN memory consumption and runtime.
problem Efficiently training large-scale GNNs with reduced memory usage.
method Block-wise quantization of intermediate activation maps with improved variance minimization.
result Further reduction in memory consumption (>15%) and runtime speedup (5%) with similar performance trade-offs.
FRD protects privacy in distributed RL by sharing proxy experience memory.
problem Privacy violation in exchanging experience memory in distributed RL.
method Proposes FRD framework using proxy experience memory.
result Numerical evaluation shows FRD is effective and performance depends on proxy memory structure.
DAM with MRL improves relational reasoning in MANNs.
problem Limited performance of associative memory networks on complex relational reasoning tasks.
method Distributed Associative Memory architecture with Memory Refreshing Loss.
result Enhanced relation reasoning performance of MANNs on long temporal sequence data.
SMT improves robotic long-horizon tasks by embedding and utilizing past observations.
problem Long-horizon tasks in partially observable environments require effective long-term memory.
method Scene Memory Transformer (SMT) embeds and uses attention to exploit spatio-temporal dependencies.
result SMT outperforms existing policies in visual navigation tasks.
Generative diffusion models mimic biological memory networks, encoding associative dynamics in deep neural weights.
problem Understanding long-term memory mechanisms in neuroscience and AI.
method Interpreting generative diffusion models as energy-based models and comparing them to Hopfield networks.
result Generative diffusion models can encode associative dynamics of Hopfield networks in deep neural weights.
Meta-learning agents forget tasks; this work creates a memory system to help them remember.
problem Meta-learning agents forget previously learned tasks when new ones appear.
method Developed a meta-learning architecture that combines LSTM and a differentiable neural episodic memory.
result Agents with the episodic LSTM architecture can solve tasks more effectively when they recur.
Generative memory model avoids vanishing gradients to robustly retrieve patterns.
problem Robust retrieval of stored patterns in the presence of interference and noise.
method Training a generative distributed memory without explicitly simulating attractor dynamics, using a likelihood-based Lyapunov function.
result The model converges to correct patterns upon iterative retrieval and achieves competitive performance as a memory model and a generative model.
NeuZip compresses neural network weights to save memory during training and inference.
problem Memory constraints in neural network training and inference.
method Entropy-based dynamic weight compression.
result Significant reduction in memory usage without performance loss.
The paper aims to develop a methodology to test memory systems in reinforcement learning agents.
problem Understanding and evaluating memory systems in reinforcement learning agents and their generalization.
method Construct diverse memory tasks, develop an agent architecture combining multiple memory systems, and perform ablations.
result The agent architecture can apply learned knowledge to holdout data that differs from the training data.
Transformers can store facts efficiently using associative memories.
problem Understanding how transformers store and recall factual information.
method Proved linear scaling of storage capacities for linear and MLP associative memories, introduced a synthetic task, and analyzed gradient flow.
result Shallow transformers can achieve near optimal storage capacity for factual recall tasks using associative memories.
Global Memory Augmentation (GMAT) improves Transformer performance on long documents.
problem Large memory requirements of Transformer pairwise dot-product attention for long sequences.
method Integrates a dense global memory of length M into sparse Transformer blocks.
result Significant improvement on various tasks, including synthetic tasks, masked language modeling, and reading comprehension.
Enhanced image recognition models learn from human-like memory and shape biases.
problem Improving robustness of image recognition models against various perturbations.
method Integrating human-like episodic memory and shape bias features into image recognition models.
result Combining human-like features improves robustness against both adversarial and natural perturbations.
DMs emerge from DenseAMs, transitioning from memorization to generalization.
problem Hindered memory retrieval in DenseAMs due to spurious states.
method Examined diffusion models through the lens of DenseAMs, focusing on their generative process.
result Identified a critical phase in DMs transitioning from memorization to generalization.
This work improves reinforcement learning with sparse rewards by following diverse past trajectories.
problem Challenges in reinforcement learning with sparse rewards and myopic behavior.
method Proposes a trajectory-conditioned policy to learn from a memory buffer of diverse past trajectories.
result Significantly outperforms existing methods on complex tasks with local optima.
Low-precision quantization improves kernel approximation under memory constraints.
problem Training kernel approximation methods efficiently with limited memory.
method Low-precision quantization of random Fourier features (LP-RFFs).
result LP-RFFs can match the performance of full-precision RFFs and Nyström method with significantly less memory.
New solver avoids memory issues for long differential equations.
problem Memory constraints in adaptive probabilistic ODE solvers.
method Fixed memory demands adaptive probabilistic solver using robust state estimation.
result Eliminates memory issues for long time series simulations.
Study reduces memory needs for active learning with enriched queries.
problem Expensive labeling costs in active learning.
method Introduces bounded memory active learning through enriched queries, introduces lossless sample compression.
result Can learn classifiers with bounded memory and query optimality.
CLA improves investment accuracy by leveraging past knowledge.
problem Improving investment decisions through past knowledge integration.
method CLA uses an explicit memory structure and FFNN base model, incorporating change points and contextual similarity.
result CLA significantly outperforms FFNN base models in expected return forecasting.
NEST optimizes deep learning training by placing devices efficiently across networks and memory.
problem Inefficient device placement in distributed deep learning leads to high communication and memory overhead.
method NEST uses network-, compute-, and memory-aware dynamic programming to optimize device placement.
result NEST achieves up to 2.43 times higher throughput and better memory efficiency.
New neural operators model turbulence with memory and randomness.
problem Modeling turbulence in complex fluid dynamics with memory and randomness.
method Symmetrized activation functions, fractional derivatives, and stochastic noise.
result Theoretical guarantees for approximation quality in turbulent phenomena.
New method uses neural networks to create models with memory effects.
problem Accurately modeling memory effects in reduced models.
method Analogies between recurrent neural networks and Mori-Zwanzig formalism to develop reduced models with memory.
result The proposed method produces reduced models with good performance on short-term and long-term predictions.
Tiny episodic memory significantly improves continual learning performance.
problem Transfer knowledge between tasks in continual learning.
method Store and replay a small number of examples from previous tasks.
result A simple baseline outperforms CL approaches with episodic memory.
PipeDream-2BW accelerates large model training by 20x with minimal memory usage.
problem Training large models requires memory beyond single accelerator capacity.
method Pipeline parallelism, weight gradient coalescing, double buffering.
result Accelerates large model training by up to 20x.
TentacleNet improves binarized CNNs, reducing accuracy loss and memory usage.
problem Excessive accuracy loss in binarized CNNs.
method Parallelization inspired by ensemble learning theory, end-to-end trainable compact topology.
result Significant memory savings compared to state-of-the-art binary ensemble methods.
This research unifies concepts of fading memory in RNNs.
problem Unclear relationships between fading memory concepts in RNNs.
method Unified language and new proofs for fading memory concepts.
result Clarified relationships between fading memory concepts.
New approach reduces malware detection memory requirements and speeds up training.
problem Efficiently classifying long sequences of malware detection data.
method Developed a new temporal max pooling method and global channel gating design.
result 116x more memory efficient and 25.8x faster training on original dataset.
A new memory replay mechanism improves reinforcement learning stability and speed.
problem Forgetting in reinforcement learning with continuous control.
method Augmented Memory Replay (AMR) that optimizes the replay of past experiences.
result AMR enhances stability and convergence speed of learning algorithms.
Research on long-range memory in financial and social systems using various models.
problem Understanding the nature of long-range memory in socioeconomic systems.
method Various Markov processes including point processes, stochastic differential equations, and agent-based models.
result New estimators of self-similarity and long-range memory for non-Gaussian systems are needed.
New memory architecture improves meta-learning performance in small datasets.
problem Meta-learning challenges with small datasets and memory interference.
method Introduced a Feature-Label Memory Network (FLMN) that splits memory into feature and label components.
result FLMN outperforms MANN in supervised one-shot classification tasks.
SAGE improves memory efficiency by selectively adding, merging, or ignoring new facts.
problem Efficiently managing new facts in agentic LLMs to avoid costly write-time reasoning.
method SAGE uses a von Mises-Fisher-based density estimator to score and route candidate facts.
result SAGE achieves the best average token-F1 on LoCoMo and reduces add-phase API cost by 3.4x on GPT-4o-mini.
Enhances sequence memory capacity in neural networks.
problem Limited sequence capacity in Hopfield-like neural networks.
method Introducing a nonlinear interaction term and a generalized pseudoinverse rule.
result Significantly increased sequence capacity with novel scaling laws.