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Deep Learning: Methods and Applications

A monograph surveying general deep learning methods and their applications across signal and information processing tasks.

This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. Application areas are chosen based on the authors' expertise, areas already transformed by deep learning such as speech recognition and computer vision, and areas with strong potential and research growth. The latter include natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.

Based on: Deep Learning: Methods and Applications · Foundations and Trends® in Signal Processing

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End-to-End Training of Deep Visuomotor Policies

Presents a method to jointly train perception and control end-to-end, mapping raw robot images directly to motor torques.

This paper asks whether training perception and control jointly end-to-end outperforms training each separately for robotic policy search. The authors develop a method that learns policies mapping raw image observations directly to motor torques, represented by deep convolutional networks with 92,000 parameters. Training uses a partially observed guided policy search that transforms policy search into supervised learning, supervised by a trajectory-centric reinforcement learning method. It is evaluated on real-world manipulation tasks such as screwing a cap onto a bottle.

Based on: End-to-End Training of Deep Visuomotor Policies · Journal of machine learning research

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Efficiently Modeling Long Sequences with Structured State Spaces

Introduces S4, a structured state space sequence model that efficiently captures very long-range dependencies across modalities.

S4 is a sequence model for long-range dependencies where RNNs, CNNs, and Transformers struggle at 10,000+ steps. It builds on the state space model x'=Ax+Bu, y=Cx+Du, and introduces a parameterization that conditions state matrix A with a low-rank correction, enabling stable diagonalization and reducing computation to a Cauchy kernel. This makes prior SSMs far more efficient while preserving their strengths. S4 reaches 91% on sequential CIFAR-10, narrows the gap to Transformers while generating 60x faster, and sets SOTA on Long Range Arena, including the 16k-length Path-X task.

Based on: Efficiently Modeling Long Sequences with Structured State Spaces · International Conference on Learning Representations

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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Introduces PatchTST, a patch-based, channel-independent Transformer for multivariate time series forecasting and self-supervised learning.

PatchTST is an efficient Transformer design for multivariate time series forecasting and self-supervised representation learning. It rests on two ideas: segmenting series into subseries-level patches used as input tokens, and channel-independence where each univariate channel shares embeddings and Transformer weights. Patching retains local semantics, quadratically cuts attention cost, and lets the model attend to longer history. PatchTST substantially improves long-term forecasting over prior Transformers and yields strong self-supervised pre-training and transfer results.

Based on: A Time Series is Worth 64 Words: Long-term Forecasting with Transformers · International Conference on Learning Representations

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Convolutional Networks on Graphs for Learning Molecular Fingerprints

Introduces a convolutional neural network operating directly on graphs to learn differentiable molecular fingerprints, generalizing circular fingerprints.

This work introduces a convolutional neural network that operates directly on graphs, enabling end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture generalizes standard molecular feature extraction methods based on circular fingerprints. The authors show that these data-driven learned features are more interpretable and achieve better predictive performance than the fixed circular-fingerprint baselines across a variety of tasks.

Based on: Convolutional Networks on Graphs for Learning Molecular Fingerprints · Neural Information Processing Systems

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Efficient Processing of Deep Neural Networks: A Tutorial and Survey

A tutorial and survey of hardware and algorithmic techniques for processing deep neural networks efficiently to improve energy efficiency and throughput.

Deep neural networks power many AI applications, delivering top accuracy at high computational cost. Techniques for efficient DNN processing that improve energy efficiency and throughput without sacrificing accuracy or raising hardware cost are critical to deployment. This tutorial and survey overviews DNNs, discusses hardware platforms and architectures supporting them, and highlights trends in cutting computation cost via hardware design or joint hardware-algorithm codesign. It also summarizes benchmarking metrics and design considerations for evaluating DNN hardware.

Based on: Efficient Processing of Deep Neural Networks: A Tutorial and Survey · Proceedings of the IEEE

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Understanding Black-box Predictions via Influence Functions

Uses influence functions from robust statistics to trace a model's prediction back to the training points most responsible for it.

To explain a black-box model's predictions, this work uses influence functions, a classic robust-statistics technique, to trace a prediction through the learning algorithm back to the training points most responsible for it. The authors develop an efficient implementation needing only oracle access to gradients and Hessian-vector products. Even on non-convex, non-differentiable models where the theory breaks down, approximations remain informative. On linear models and CNNs, they aid understanding behavior, debugging, detecting dataset errors, and crafting training-set attacks.

Based on: Understanding Black-box Predictions via Influence Functions · International Conference on Machine Learning

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MLP-Mixer: An all-MLP Architecture for Vision

Introduces MLP-Mixer, an all-MLP vision architecture that mixes per-patch and cross-patch features without convolutions or attention.

CNNs are the go-to vision model, and attention-based Vision Transformers are popular, but this paper shows neither convolutions nor attention are necessary. MLP-Mixer is built exclusively from multi-layer perceptrons, with two layer types: MLPs applied per patch to mix channel features, and MLPs applied across patches to mix spatial information. Trained on large datasets or with modern regularization, it attains competitive image classification scores at pre-training and inference cost comparable to state-of-the-art models, and the authors hope it sparks research beyond CNNs and Transformers.

Based on: MLP-Mixer: An all-MLP Architecture for Vision · Neural Information Processing Systems

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Mapping Identifiers for the Integration of Genomic Datasets with the R/Bioconductor package biomaRt

Presents a protocol using the R/Bioconductor biomaRt package and BioMart web services to integrate and jointly analyze diverse genomic datasets.

Genomic experiments produce multiple views of biological systems, including DNA sequence, copy number variation, and mRNA and protein abundance, requiring integrated bioinformatic analysis. Public databases like Ensembl provide mappings between probe and target molecules, but these can be complex and dynamic. This protocol shows how to use R with BioMart web services, via the biomaRt package, to integrate and jointly analyze datasets. It discusses typical gene-to-transcript-to-protein mapping problems and offers a flexible, reproducible basis for data integration.

Based on: Mapping Identifiers for the Integration of Genomic Datasets with the R/Bioconductor package biomaRt · Nature Protocols

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ClinVar: improving access to variant interpretations and supporting evidence

Describes updates to ClinVar, a public NIH archive of human genetic variants and their clinical significance, improving data access and search.

ClinVar is a freely available public NIH archive of human genetic variants and interpretations of their significance to disease. Interpretations are submitted by clinical and research labs, expert panels, and others, and data are aggregated by variant-disease pairs and by variant, accessible via the website, improved VCF files, and a new XML report. ClinVar recently began accepting phenotype-focused submissions, including provider interpretations and phenotyping-only data, and continues improving search with new indexed fields and filters.

Based on: ClinVar: improving access to variant interpretations and supporting evidence · Nucleic Acids Res.

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Open Graph Benchmark: Datasets for Machine Learning on Graphs

Presents OGB, a diverse suite of large-scale, realistic benchmark datasets with unified evaluation protocols for reproducible graph machine learning.

The Open Graph Benchmark (OGB) is a diverse set of challenging, realistic datasets for reproducible graph ML research. Datasets are large-scale (up to 100M+ nodes, 1B+ edges), span multiple tasks, and cover social, information, biological, molecular, source-code AST, and knowledge-graph domains. Each provides a unified evaluation protocol with application-specific splits and metrics. Experiments reveal scaling and out-of-distribution generalization challenges, and OGB offers an automated pipeline with public loaders and leaderboards.

Based on: Open Graph Benchmark: Datasets for Machine Learning on Graphs · Neural Information Processing Systems

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DreamFusion: Text-to-3D using 2D Diffusion

Introduces text-to-3D synthesis by distilling a pretrained 2D text-to-image diffusion model to optimize a NeRF, needing no 3D training data.

Text-to-image diffusion models have driven synthesis breakthroughs, but 3D adaptation would need large labeled 3D datasets and efficient 3D denoisers, neither of which exist. DreamFusion instead uses a pretrained 2D text-to-image diffusion model for text-to-3D. It introduces a probability-density-distillation loss letting the 2D model act as a prior, and via a DeepDream-like procedure optimizes a randomly initialized NeRF so its random-angle renderings score well. The resulting 3D model can be viewed from any angle, relit, and composited, requiring no 3D data or model modifications.

Based on: DreamFusion: Text-to-3D using 2D Diffusion · International Conference on Learning Representations

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