RoundPipe achieves near-zero-bubble pipeline parallelism for LLM training on consumer GPUs by dynamically dispatching computation stages round-robin, yielding 1.48-2.16x speedups and enabling 235B model fine-tuning on 8x RTX 4090.
super hub Mixed citations
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Mixed citation behavior. Most common role is background (53%).
156
Pith papers citing it
Background
53% of classified citations
abstract
Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.
hub tools
citation-role summary
background 18
method 11
dataset 1
citation-polarity summary
claims ledger
- abstract Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect o
co-cited works
representative citing papers
Looped transformers with recall and outer normalization produce reachable, input-dependent fixed points with stable gradients, enabling generalization, while those without recall cannot; a new internal recall variant performs competitively or better.
First in-orbit demonstration of a DRL-trained AI satellite attitude controller that performs robust inertial pointing after sim-to-real transfer.
A two-pass optimization framework with polynomial-based simulation discovers heralded ballistic circuits for 3-5 qubit graph states achieving up to 7.5x higher success probabilities than fusion baselines, including first known circuits for some 5-qubit states.
Task vectors from weight differences allow arithmetic operations to edit pre-trained models, improving multiple tasks simultaneously and enabling analogical inference on unseen tasks.
Quantitative Bayesian inference using a deep-learning emulator detects 0.018-0.020 M_sun of helium in the Type Ic supernova 2014L.
ffortissimo is a JAX-based freeform forward-modeling pipeline that fits complex dust distributions and infers scattering properties in KLIP-reduced images of circumstellar disks such as HR 4796A.
A matrix-free, GPU-compatible PyTorch implementation of phase-field fracture with explicit dynamics, custom differentiable implicit damage solve, benchmarks on dynamic and quasi-static cases, and inverse recovery of fracture energy G_c via L-BFGS.
Hybrid TimesFM plus ridge regression on covariates forecasts 1-MeV electron flux with average R² of 0.9 on out-of-sample 2024 data, outperforming linear regression, CNN, LSTM and Transformer models.
A neural network trained on simulations infers stripping times for Sagittarius stream stars from phase-space data, measuring a 0.3 dex/Gyr metallicity gradient and estimating ages for globular clusters such as Pal 12 and NGC 2419.
Events trigger on-the-fly LoRA module generation via hypernetworks over a shared team policy in MARL, paired with a Neural Manifold Diversity metric, enabling sequential role reassignment while preserving reward maximization.
Vibrational mode graphs from molecular dynamics enable sequence-free protein function prediction via graph neural networks, with entrainment improving signals for collective dynamics.
Dingo-Pop uses a transformer to perform amortized, end-to-end population inference from GW strain data in seconds, bypassing per-event Monte Carlo sampling.
Learning in low-rank RNNs reduces to an exact low-dimensional ODE system in overlap space, where loss-invisible overlaps encode training history without affecting function.
Dynamical magnetotropic susceptibility k(ω) acts as a probe of uniform spin and charge fluctuations, with its static scaling in α-RuCl3 arising specifically from dominant Kitaev interactions in the models examined.
Transformer networks sample up to 180x180 2D Ising systems and 64x64 Edwards-Anderson systems by generating spin groups with probability approximations, yielding ~20x higher effective sample size than prior neural samplers at criticality.
A single end-to-end Transformer model unifies stellar labels from heterogeneous spectroscopic surveys into a self-consistent scale without post-hoc recalibration.
Parametric neural networks learn likelihood ratios to infer top-philic scalar resonances from dip patterns caused by signal-background interference in hadron collider data.
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.
Concept Graph Convolutions perform message passing on node concepts to increase interpretability of graph neural networks without losing task performance.
A neural network learns non-stationary anisotropic correlations from gridded CTM outputs and transfers the structure via LatticeKrig basis functions to station data for refined fine-scale NO2 predictions with uncertainty.
MOFAT applied to SN2024ggi shows CO triggering inner SiO formation with a receding edge, order-of-magnitude mass drop, clumping signatures, and no dust formation.
A new partitioning algorithm that provably load-balances arbitrary sparse tensor algebra expressions by generalizing parallel merging to multi-operand, multi-dimensional hierarchical structures, implemented in a compiler framework.
citing papers explorer
-
Automated discovery of heralded ballistic graph state generators for fusion-based photonic quantum computation
A two-pass optimization framework with polynomial-based simulation discovers heralded ballistic circuits for 3-5 qubit graph states achieving up to 7.5x higher success probabilities than fusion baselines, including first known circuits for some 5-qubit states.
-
Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
-
Meson spectroscopy of exotic symmetries of Ising criticality in Rydberg atom arrays
Rydberg arrays realize Ising criticality with E8 mass spectra in chains and first signatures of D8^(1)-organized bound states from interchain confinement in ladders.
-
Universal Jaynes-Cummings Control of an Oscillator
Experimental demonstration of universal qudit control on a cavity oscillator via compiled Jaynes-Cummings gates with a transmon ancilla, reaching 96% mean post-selected process fidelity for qutrit gates.
-
Optimizing Quantum Photonic Integrated Circuits using Differentiable Tensor Networks
Gradient-based optimization of quantum photonic circuits is achieved via differentiable tensor networks that model nonlinear unitary gates and stochastic losses at low photon numbers.
-
Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
-
Tensor-Programmable Quantum Circuits for Solving Differential Equations
A quantum solver for PDEs is introduced via flexible matrix product operator representations with mid-circuit measurements and state-dependent norm correction to handle non-unitary dynamics.
-
Variational decision diagrams for quantum-inspired machine learning applications
The paper proposes variational decision diagrams (VDDs) for quantum state representation in QML and reports successful training without barren plateaus on transverse-field Ising and Heisenberg Hamiltonians.
-
Compositional Quantum Heuristics for Max-Clique Detection
Compositional quantum circuits with symmetry-induced invariant losses produce trainable equivariant quantum GNNs that generalize on max-clique problems and improve hybrid recursive search accuracy and scalability.
-
Molecular Quantum Control Algorithm Design by Reinforcement Learning
Reinforcement learning designs quantum-logic pulse sequences to prepare H3O+ (130 states) and CaH+ molecular ions in pure states from thermal populations.
- MCMit: Mid-Circuit Measurement Error Mitigation