Read Advances in Neural Information Processing Systems: v. 8: Proceedings of the The complete twelve-volume proceedings of the Neural Information Processing Systems conferences from 1988 to 1999 on CD-ROM. Most Cited Authors. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. ��_te���w���,&RL����s$m%P��O-0i"8��*�M��2�v�hzg1�*����,>��``�l�g���)yrA��-�v���O�T� K�3�f��=x�)e`iQ��2��E+X�,��2. Neural Information Processing Systems (NIPS) 2008. First, the complete Proceedings will … Graf and others published Advances in Neural Information Processing Systems | Find, read and cite all the research you need on ResearchGate %PDF-1.7 %���� are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems.A Bradford Book. In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. SPEDIZIONE GRATUITA su ordini idonei (ISBN: 9780262561457) from Amazon's Book Store. The annual Neural Information Processing (NIPS) meeting is the flagship conference on neural computation. 182 0 obj <>/Filter/FlateDecode/ID[]/Index[172 25]/Info 171 0 R/Length 64/Prev 277477/Root 173 0 R/Size 197/Type/XRef/W[1 2 1]>>stream ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 12 Proceedings of the 1999 Conference edited by Sara A. Solla, Todd K. Leen and Klaus-Robert Müller A Bradford Book The MIT Press Cambridge, Massachusetts London, England Buy Advances in Neural Information Processing Systems: Proceedings of the First 12 Conferences (Neural Information Processing Series) (The MIT Press) Cdr by Jordan, Michael I., Lecun, Yann, Solla, Sara A. �%� Bartlett, Peter, Pereira, Fernando, Burges, Christopher, Bottou, Leon, & Weinberger, Kilian (Eds.) The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstration We present a formulation of CNNs in the context of spectral graph theory, which provides the … Online control of the false discovery rate with decaying memory, Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes, Imagination-Augmented Agents for Deep Reinforcement Learning, Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations, Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning, Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra, Asynchronous Parallel Coordinate Minimization for MAP Inference, Multiscale Quantization for Fast Similarity Search, Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space, Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods, Training Quantized Nets: A Deeper Understanding, Permutation-based Causal Inference Algorithms with Interventions, Time-dependent spatially varying graphical models, with application to brain fMRI data analysis, Gradient Methods for Submodular Maximization, Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization, The Importance of Communities for Learning to Influence, Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos, Learning Neural Representations of Human Cognition across Many fMRI Studies, A KL-LUCB algorithm for Large-Scale Crowdsourcing, Collaborative Deep Learning in Fixed Topology Networks, Learning Disentangled Representations with Semi-Supervised Deep Generative Models, Self-Supervised Intrinsic Image Decomposition, Exploring Generalization in Deep Learning, A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control, Fader Networks: Manipulating Images by Sliding Attributes, Estimating Mutual Information for Discrete-Continuous Mixtures, Parameter-Free Online Learning via Model Selection, Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction, Unbounded cache model for online language modeling with open vocabulary, Predictive State Recurrent Neural Networks, Early stopping for kernel boosting algorithms: A general analysis with localized complexities, SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability, Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma, A Learning Error Analysis for Structured Prediction with Approximate Inference, Efficient Second-Order Online Kernel Learning with Adaptive Embedding, Implicit Regularization in Matrix Factorization, Optimal Shrinkage of Singular Values Under Random Data Contamination, Countering Feedback Delays in Multi-Agent Learning, Asynchronous Coordinate Descent under More Realistic Assumptions, Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls, Hierarchical Clustering Beyond the Worst-Case, Invariance and Stability of Deep Convolutional Representations, The Expressive Power of Neural Networks: A View from the Width, Spectrally-normalized margin bounds for neural networks, Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes, Population Matching Discrepancy and Applications in Deep Learning, Scalable Planning with Tensorflow for Hybrid Nonlinear Domains, Learned in Translation: Contextualized Word Vectors, Scalable Log Determinants for Gaussian Process Kernel Learning, Poincaré Embeddings for Learning Hierarchical Representations, Learning Combinatorial Optimization Algorithms over Graphs, Learning with Bandit Feedback in Potential Games, Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments, Communication-Efficient Distributed Learning of Discrete Distributions, Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness, Matrix Norm Estimation from a Few Entries, Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons, Causal Effect Inference with Deep Latent-Variable Models, Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity, Gradient Episodic Memory for Continual Learning, Effective Parallelisation for Machine Learning, Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding, Clustering Stable Instances of Euclidean k-means, Good Semi-supervised Learning That Requires a Bad GAN, On Blackbox Backpropagation and Jacobian Sensing, Protein Interface Prediction using Graph Convolutional Networks, Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities, Towards Generalization and Simplicity in Continuous Control, Random Projection Filter Bank for Time Series Data, On Frank-Wolfe and Equilibrium Computation, Modulating early visual processing by language, Learning Mixture of Gaussians with Streaming Data, Practical Hash Functions for Similarity Estimation and Dimensionality Reduction, GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, The Scaling Limit of High-Dimensional Online Independent Component Analysis, The power of absolute discounting: all-dimensional distribution estimation, Spectral Mixture Kernels for Multi-Output Gaussian Processes, Learning Linear Dynamical Systems via Spectral Filtering, Z-Forcing: Training Stochastic Recurrent Networks, Learning Hierarchical Information Flow with Recurrent Neural Modules, Neural Variational Inference and Learning in Undirected Graphical Models, The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process, Structured Bayesian Pruning via Log-Normal Multiplicative Noise, Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin, Acceleration and Averaging in Stochastic Descent Dynamics, Kernel functions based on triplet comparisons, An Error Detection and Correction Framework for Connectomics, Style Transfer from Non-Parallel Text by Cross-Alignment, Stochastic Submodular Maximization: The Case of Coverage Functions, Affinity Clustering: Hierarchical Clustering at Scale, Unsupervised Transformation Learning via Convex Relaxations, A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening, Linear Time Computation of Moments in Sum-Product Networks, A Meta-Learning Perspective on Cold-Start Recommendations for Items, Predicting Scene Parsing and Motion Dynamics in the Future, Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference, Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification, Kernel Feature Selection via Conditional Covariance Minimization, Convergence of Gradient EM on Multi-component Mixture of Gaussians, Real Time Image Saliency for Black Box Classifiers, Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples, Efficient and Flexible Inference for Stochastic Systems, When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent, Experimental Design for Learning Causal Graphs with Latent Variables, Stochastic Mirror Descent in Variationally Coherent Optimization Problems, On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models, A General Framework for Robust Interactive Learning, Multi-view Matrix Factorization for Linear Dynamical System Estimation. 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