A new LSTM model reduces state updates and improves convergence for long sequences.
problem Vanishing gradient problem in RNNs and slow convergence on long sequences.
method Gaussian-gated LSTM (g-LSTM) with a time gate to control neuron updates.
result The g-LSTM model reduces state updates and computes by at least 10x compared to an equivalent LSTM.
This paper introduces channel gating, a dynamic, fine-grained, and hardware-efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs). Channel gating identifies regions in the features that contribute less to the classification result, and skips the computation on a subset of the …
The paper studies 4-qubit Clifford states and their properties.
problem Understanding the set and properties of 4-qubit Clifford states.
method Analyzing the 293760 4-qubit Clifford states, splitting them into 18 groups, and studying the action of CNOT gates and local gates.
result There are 293760 4-qubit Clifford states with specific entanglement entropies, and any pair can be connected with local gates and at most 3 CNOT gates.
Reduced-gate LSTM improves spatiotemporal prediction with less parameters.
problem Next-frame video prediction in deep learning.
method Predictive coding framework with reduced-gate convolutional LSTM.
result Reduced-gate model achieves equal or better accuracy with fewer parameters.
Improved GRU model with weighted time-delay feedback for long-term dependencies.
problem Modeling long-term dependencies in sequential data.
method Introducing a gated recurrent unit (GRU) with a weighted time-delay feedback mechanism.
result τ-GRU outperforms state-of-the-art models on various tasks.
In quantum computation, series of quantum gates have to be arranged in a predefined sequence that led to a quantum circuit in order to solve a particular problem. What if the sequence of quantum gates is known but both the problem to be solved and the outcome of the so defined quantum circuit remain in the shadow? This…
DeepESNs outperform ESN and GRUs in multivariate time-series prediction.
problem Comparing DeepESNs and gated RNNs for multivariate time-series prediction.
method Experimental comparison of DeepESNs and gated RNNs (Gated Recurrent Units, Long Short-Term Memory) on 4 polyphonic music tasks.
result DeepESNs outperform ESN and GRUs in terms of prediction accuracy and efficiency.
Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use ad hoc gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues. We prove that learnable gates in a…
GCRNNs improve graph problem solving with fewer parameters.
problem Graph process problems like earthquake epicenter identification and weather prediction.
method GCRNNs use convolutional filter banks and time-gated variations of GCRNNs (Gated GCRNNs) to improve performance.
result GCRNNs significantly improve performance over GNNs and another graph recurrent architecture.
Model accurately gates ocean microbes from high-frequency flow cytometry data.
problem Gating of high-frequency flow cytometry data for ocean microbes is challenging.
method Trend filtered mixture of experts with smooth parameter variation.
result Model accurately matches human-annotated gating and corrects errors.
New algorithm trains ReLU gates provably in linear time.
problem Training ReLU gates in realizable settings with mild conditions.
method Iterative stochastic algorithm with moment assumptions.
result First recovery of true labels under data-poisoning attacks.
New interpretation of RNN forget gate improves learnability for long-term sequential data.
problem Improving learnability of recurrent neural networks for long-term temporal dependencies.
method Generalized theory of gated RNNs, focusing on gradient behavior over time.
result Existing RNNs satisfy the gradient condition for initial training, suggesting validity of forget gate interpretation.
mGRN improves multivariate time series prediction by managing marginal and joint memories.
problem Extracting dependencies in multivariate sequential data with strong serial and cross-sectional dependencies.
method Developed a novel recurrent network architecture, Memory-Gated Recurrent Networks (mGRN), with gates for marginal and joint memories.
result mGRN consistently outperforms state-of-the-art architectures on various public datasets.
FC-GAGA forecasts traffic using a novel gating mechanism.
problem Forecasting multivariate time-series, especially with graph relationships.
method Learnable fully connected hard graph gating mechanism for fully connected time-series forecasting.
result Competitive or better performance than existing algorithms without graph knowledge.
We propose a novel adaptive approximation approach for test-time resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the input among a collection of models. Our objective is to minimize overall average cost without sacrificing accuracy. We learn gat…
Paper connects MoE and self-attention, proposing active-attention.
problem Improving efficiency and performance of self-attention mechanisms.
method Established connection between MoE and self-attention, analyzed quadratic gating functions, proposed active-attention mechanism.
result Active-attention outperforms standard self-attention in various tasks.
Recurrent Neural Networks (RNNs) play a major role in the field of sequential learning, and have outperformed traditional algorithms on many benchmarks. Training deep RNNs still remains a challenge, and most of the state-of-the-art models are structured with a transition depth of 2-4 layers. Recurrent Highway Networks …
A trainable gate optimizes neural network selection and pruning.
problem Optimizing neural networks for specific tasks.
method Introduces a trainable gate function to make discrete selection problems differentiable.
result Efficiently optimizes arbitrary neural networks across various tasks.
Complex numbers have long been favoured for digital signal processing, yet complex representations rarely appear in deep learning architectures. RNNs, widely used to process time series and sequence information, could greatly benefit from complex representations. We present a novel complex gated recurrent cell, which i…
ABF-T-GLCP forecasts and calibrates uncertainty for multivariate nonstationary time series.
problem Forecasting and uncertainty quantification in nonstationary multivariate time series.
method Adaptive multi-scale forecasting and Gate-Localized Conformal Prediction.
result Consistent gains in point forecasting accuracy and narrower prediction intervals with close empirical coverage.
AECF improves multimodal inference robustness and calibration.
problem Robustness and calibration issues in multimodal systems with missing inputs.
method Adaptive Entropy-Gated Contrastive Fusion (AECF) layer.
result Improves masked-input mAP by +18 pp at a 50% drop rate.
New protocols implement logical gates on encoded qubits with minimal overhead.
problem Efficiently performing universal logical gates on encoded qubits with minimal overhead.
method Using topological codes associated to hyperbolic surfaces, we introduce protocols to implement Dehn twists through constant depth unitary circuits.
result Demonstrated the possibility of applying universal logical gate sets on encoded qubits through constant depth unitary circuits and with constant space overhead.
Improved logistic MoE with sigmoid gate shows better sample efficiency.
problem Improving sample efficiency in logistic MoE models.
method Comprehensive analysis of multinomial logistic MoE with modified sigmoid gate, incorporating temperature parameter and using Euclidean score.
result The sigmoid gate leads to lower sample complexity than softmax gate for both parameter and expert estimation.
New method prepares 3-qubit states using local gates and controlled-Z gates.
problem Preparation of 3-qubit states using quantum gates.
method Uses Ry(θ) gates and controlled-Z gates, with an optimal number of controlled-Z gates. result Optimal number of controlled-Z gates for preparing 3-qubit states is four. We present a dynamic model selection approach for resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the input among a collection of models. Our objective is to minimize overall average cost without sacrificing accuracy. We learn gating and predict…
Study stability of selective SSMs with discontinuous gating.
problem Challenges in stability analysis of selective SSMs with discontinuous gating.
method Passivity and Input-to-State Stability (ISS) analysis of continuous-time selective SSMs.
result Derivation of sufficient conditions for global ISS with respect to the port input.
Sigmoid gating is more sample efficient than softmax in mixture of experts.
problem Softmax gating leads to unnecessary competition among experts, causing representation collapse.
method Theoretical analysis of a regression framework with mixture of experts, identifying identifiability conditions and convergence rates.
result Sigmoid gating requires fewer samples to achieve the same expert estimation error as softmax gating.
Proposes a new LSTM gate structure using bivariate Beta distribution.
problem Inflexibility of sigmoid gates in modeling multi-modality and skewness, and lack of modeling correlation between gates.
method Introduces a bivariate Beta distribution gate structure within LSTM cells.
result Empirically shows higher gradient values and improved model performance.
Study reveals biases in gradient descent for GLNs, improving neural network performance.
problem Understanding and improving the inductive biases of deep neural networks.
method Derive infinite-time training limit of gated linear networks and generalize to other networks.
result Theoretical framework captures key inductive biases of ReLU networks.
Improved time series classification with GRU-FCN model.
problem Time series classification challenges.
method Hybrid LSTM-GRU model for univariate time series classification.
result GRU-FCN model outperforms state-of-the-art models.
Temporal Functional Circuits explain KAN forecasts with interpretable edge functions.
problem Lack of mechanistic explanations in KAN forecasting.
method Transform KAN edge functions into faithful, temporally grounded explanations using a gated residual KAN.
result Gated KAN achieves lower MSE than linear-only models on regime-switching signals.
Bayesian method sparsifies gated RNNs, improving speed and interpretability.
problem Sparsifying neural networks to reduce complexity and improve performance.
method Bayesian approach to sparsify weights, neurons, and gates in LSTM architectures.
result Sparsified gated RNNs speed up forward pass and improve compression.
We sparsify gated RNNs by simplifying their structure.
problem Improving efficiency of RNNs by reducing their complexity.
method Adjust existing sparsification techniques to gated RNNs, sparsifying preactivations of gates.
result Simplified LSTM structure improves model performance and efficiency.
Gated attention improves model curvature, enhancing performance on nonlinear tasks.
problem Understanding the geometric implications of gating in attention mechanisms.
method Modeling attention outputs as Gaussian distributions and analyzing Fisher--Rao geometry.
result Gated attention enables non-flat geometries, including positively curved manifolds.
Faster ZSL with continual learning and self-gating.
problem Generalizing models to unseen categories and handling sequential data.
method Meta-continual zero-shot learning (MCZSL) with self-gating and scaled class normalization.
result Outperforms state-of-the-art results with faster training (>100imes). Gated attention improves performance by using a hierarchical mixture of experts.
problem Improving performance of self-attention mechanisms in Transformers.
method Rigorously show that gated attention can be modeled as a hierarchical mixture of experts, providing a theoretical justification for its benefits.
result Gated attention is more sample-efficient than multi-head self-attention, requiring fewer data points to achieve the same estimation error.
Given the success of the gated recurrent unit, a natural question is whether all the gates of the long short-term memory (LSTM) network are necessary. Previous research has shown that the forget gate is one of the most important gates in the LSTM. Here we show that a forget-gate-only version of the LSTM with chrono-ini…
Gating units in GRUs and LSTMs create slow modes and control phase-space complexity.
problem Training challenges in RNNs due to exploding or vanishing gradients.
method Random matrix theory and mean-field theory applied to GRUs and LSTMs.
result Gates in GRUs and LSTMs lead to accumulation of slow modes and control phase-space complexity.
Improved HMoE models using Laplace gating function enhance expert specialization and performance.
problem Improving performance of hierarchical mixture of experts models.
method Used Laplace gating function instead of Softmax in hierarchical mixture of experts models.
result Laplace gating function accelerates expert convergence and enhances specialization.
GRUs exhibit diverse dynamical behaviors but cannot mimic continuous attractors.
problem Understanding and predicting the dynamics of GRUs for neural data.
method Continuous time dynamical systems analysis of GRU networks.
result GRUs can represent stable limit cycles, multi-stable dynamics, and homoclinic bifurcations but not continuous attractors.
LSTM improves cross-network recommendations by capturing user preference changes and irregular time intervals.
problem Offline cross-network recommender solutions fail to capture user preference changes and dynamic environments.
method Proposes a multi-layered LSTM network with attention mechanisms, higher order interactions, and time-aware gates.
result The model consistently outperforms state-of-the-art in accuracy, diversity, and novelty.
The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while…
This study analyzes communication constraints in MoE architectures using information theory.
problem Communication constraints in Mixture-of-Experts (MoE) architectures.
method Developed a rate-distortion characterization of finite-rate gating in MoE architectures using information theory.
result Yielded capacity-aware limits for communication-constrained MoE systems.
Improved LSTM cell for high-frequency trading forecasts.
problem Precise stock price forecasting with minimal lags.
method Revised long short-term memory (LSTM) cell with optimal gate/state selection.
result Lower forecasting error compared to other recurrent neural networks.
This paper analyzes how gradient descent can learn parameters in mixture-of-experts models with gating.
problem Understanding parameter recovery in mixture-of-experts models with gating.
method Careful analysis of the optimization landscape and design of two distinct loss functions.
result Gradient descent can accurately learn parameters in mixture-of-experts models with gating.
GLCB uses Gated Linear Networks for online contextual bandits.
problem Online learning in contextual bandits with uncertainty estimation.
method Gated Linear Networks (GLNs) for prediction and uncertainty estimation.
result GLCB outperforms state-of-the-art methods in online contextual bandits.
Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data. Inside these networks, gates are used to control the flow of information, allowing to model even very long-term dependencies in the data. In this paper, we investigate whether the original gate equation (a linear projec…
Solves challenges in estimating parameters of softmax gating Gaussian mixture models.
problem Identifiability issues and complex interactions in Gaussian mixture of experts.
method Proposes novel Voronoi loss functions and establishes convergence rates of MLE.
result Connects convergence rate of MLE to a solvability problem of polynomial equations.