Predicts routing congestion in FPGA designs using machine learning.
problem Routing congestion estimation is absent or inaccurate in HLS design methods.
method Machine learning to predict routing congestion in HLS.
result Accurately estimates routing congestion with errors of 6.71% and 10.05%.
Accelerator synthesizes deep learning inference from C code using FPGA hardware.
problem Efficiently implementing deep learning models on FPGAs.
method Parallelized C code using Pthreads, high-level synthesis, reduced precision, zero-weight-skipping.
result Peak performance of 138 effective GOPS on VGG-16 on Intel Arria 10 SoC FPGA.
Study resilience of NN accelerators, especially fault characterization and mitigation.
problem Faults in hardware accelerators of NNs, especially at 10nm technology node.
method Characterized fault vulnerability of RTL NN components and developed a fault mitigation technique.
result Fault mitigation technique improves accuracy by 47.3%.
FPGAs enable real-time neural network inference for particle physics.
problem Low-latency, low-power requirements for particle physics.
method Developed hls4ml for building machine learning models in FPGAs.
result Neural network inference fits within modern FPGA resources with 100 ns latency.
A new HL-SVR approach handles unequal sample sizes in SVR for engineering data modeling.
problem SVR assumes equal sample sizes, but unequal sizes are common in engineering.
method HL-SVR combines low-level SVR for larger samples and high-level SVR for smaller samples.
result HL-SVR produces more accurate predictions than conventional SVR.
LeFlow enables quick FPGA synthesis from Tensorflow models.
problem Manual translation of Tensorflow models to FPGA RTL is time-consuming and requires expertise.
method Uses XLA compiler to emit synthesizable LLVM code from Tensorflow specifications, which is then synthesized.
result Allows users to generate Deep Neural Networks with just a few lines of Python code.
PATOIS synthesizes code from natural language using learned code idioms.
problem Synthesizing general-purpose source code from natural language specifications is challenging.
method PATOIS uses a neural synthesizer that interleaves high-level and low-level reasoning, incorporating learned code idioms from a corpus.
result Using learned code idioms improves the synthesizer's accuracy on semantic parsing datasets.
T-KAN improves HFT LOB forecasting with learnable splines.
problem Alpha decay in HFT LOB forecasting models.
method T-KAN uses learnable B-spline activation functions to model market signals.
result 19.1% relative improvement in F1-score at k = 100 horizon.
A handlebody-link is a disjoint union of embeddings of handlebodies in S3 and an HL-homotopy is an equivalence relation on handlebody-links generated by self-crossing changes. The second author and Ryo Nikkuni classified the set of HL-homotopy classes of 2-component handlebody-links completely using the linking numb…
Neural network synthesizes percussive sounds with adjustable timbral features.
problem Control over high-level timbral characteristics of percussive sounds.
method Feedforward convolutional neural network mapping input parameters to waveform.
result Changing input parameters produces a waveform congruent with desired characteristics.
HOUDINI learns algorithms across domains using program synthesis.
problem Lifelong learning of algorithmic tasks mixing perception and reasoning.
method Combining gradient descent with combinatorial search over programs.
result HOUDINI transfers high-level concepts more effectively than traditional methods.
Framework for verifying deep learning operators.
problem Complexity and errors in designing and implementing custom operators.
method Symbolic execution, syntax-guided synthesis, SMT-based verification.
result Effective synthesis and verification of deep learning operators.
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an…
This paper analyzes the conflict between Hamming loss and subset accuracy in multi-label classification.
problem The conflict between Hamming loss and subset accuracy in multi-label classification.
method The paper analyzes the learning guarantees of algorithms optimizing Hamming loss and subset accuracy, providing theoretical bounds and experimental support.
result Optimizing Hamming loss with its surrogate loss can lead to good performance on subset accuracy in small label spaces, contrary to theoretical expectations.
MORL uses program synthesis to improve reinforcement learning policies.
problem Difficult to interpret and impose constraints on learned policies from black-box neural networks.
method Iterative framework combining program synthesis and behavior cloning.
result Programmatic representation allows for high-level modifications leading to improved learning.
Programmatic Motion Concepts learn human actions from paired videos.
problem Learning motion concepts from paired video and action sequences.
method Semi-supervised learning architecture for hierarchical motion representation.
result Outperforms established baselines, especially in small data settings.
HL algorithms improve resource allocation in cloud environments.
problem Sequential decision-making under uncertainty with exogenous variables.
method HL algorithms leverage exogenous variable samples to infer counterfactual consequences.
result HL algorithms outperform classic methods and reinforcement learning in resource allocation.
We argue that Horava-Lifshitz (HL) gravity provides the minimal holographic dual for Lifshitz-type field theories with anisotropic scaling and dynamical exponent z. First we show that Lifshitz spacetimes are vacuum solutions of HL gravity, without need for additional matter. Then we perform holographic renormalization …
The topology of the Hausdorff leaf spaces (HLS) for a codim-1 foliation is the main topic of this paper. At the beginning, the connection between the Hausdorff leaf space and a warped foliations is examined. Next, the author describes the HLS for all basic constructions of foliations such as transversal and tangential …
Consider L a regular Lagrangian, S the canonical semispray, and h the horizontal projector of the canonical nonlinear connection. We prove that if the Lagrangian is constant along the integral curves of the Euler-Lagrange equations then it is constant along the horizontal curves of the canonical nonlinear connect…
A fundamental challenge in developing high-impact machine learning technologies is balancing the need to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge graphs and the Web, to…
The purpose of this paper is to introduce Harvey-Lawson manifolds and review the construction of certain mirror dual Calabi-Yau submanifolds inside a G_2 manifold. More specifically, given a Harvey-Lawson manifold HL, we explain how to assign a pair of tangent bundle valued 2 and 3-forms to a G_2 manifold (M,HL, \varph…
CARL controls a quadruped to move naturally in complex environments.
problem Motion synthesis in dynamic environments with complex constraints.
method CARL uses GANs to adapt high-level controls to action distributions and deep reinforcement learning for dynamic recovery.
result CARL can be controlled with high-level directives and react naturally to dynamic environments.
Improved diffusion models for image synthesis with better training dynamics.
problem Uneven and ineffective training in diffusion models.
method Redesigned network layers to preserve activation, weight, and update magnitudes.
result Significantly better networks at equal computational complexity, improving FID to 1.81.
DeepSynth synthesizes automata to guide deep RL agents through sparse, non-Markovian rewards.
problem Training deep RL agents with sparse, non-Markovian rewards and unknown high-level objectives.
method Employing a novel algorithm for synthesizing compact automata to uncover sequential structure from trace data.
result Reduces the number of iterations required for policy synthesis by two orders of magnitude and improves scalability.
IReEn reveals functionality of black-box agents via iterative neural synthesis.
problem Revealing the functionality of a black-box agent without privileged information.
method Iterative refinement of candidate programs using neural program synthesis.
result The approach finds a functional equivalent program in 78% of cases, outperforming state-of-the-art.
Generates long programs from inputs, optimizing multiple tasks.
problem Creating long programs from input-output pairs.
method Trains a neural network to map state and outputs to next program statement, optimizing multiple tasks concurrently.
result Creates programs twice as long as existing solutions, improving success rate and runtime.
The paper proves stability of the positive mass theorem using intrinsic flat convergence.
problem Stability of the positive mass theorem in mathematical relativity.
method Intrinsic flat convergence of points and applications to stability.
result Revisits and strengthens the stability results for graphical hypersurfaces of Euclidean space.
HL-VAE extends VAE for heterogeneous temporal and longitudinal data.
problem Handling heterogeneous data in temporal and longitudinal datasets.
method Proposes HL-VAE, an extension of existing VAEs for temporal and longitudinal data, incorporating likelihood models for various data types.
result HL-VAE achieves competitive performance in missing value imputation and predictive accuracy.
PLANS synthesizes programs from noisy inputs using neural specs and filtering.
problem Synthesizing robust programs from noisy, raw inputs.
method Hybrid model combining neural extraction and rule-based synthesis with noise filtering.
result State-of-the-art performance in diverse environments with no ground-truth training.
A fast voice conversion method using diffusion models.
problem One-shot many-to-many voice conversion.
method Diffusion probabilistic modeling with Fast Maximum Likelihood Sampling Scheme.
result Superior quality compared to state-of-the-art approaches.
Grasping is a complex process involving knowledge of the object, the surroundings, and of oneself. While humans are able to integrate and process all of the sensory information required for performing this task, equipping machines with this capability is an extremely challenging endeavor. In this paper, we investigate …
3-dimensional Harvey Lawson submanifolds were introduced in an earlier paper by Akbulut-Salur, as examples of Lagrangian-type manifolds inside G2 manifold. In this paper, we first show that the space of deformations of a smooth, compact, orientable Harvey-Lawson submanifold HL in a G2 manifold M can be identified with …
Paper develops robust methods for large-scale testing without tuning parameters.
problem Heavy-tailed data in high-dimensional settings.
method Revisits Hodges-Lehmann estimator for robust inference without tuning parameters.
result Develops confidence intervals and controls false discovery proportion.
Study on existence of ground states for free energy on hyperbolic space.
problem Existence of ground states for a free energy functional on hyperbolic space.
method Derived HLS-type inequalities on Cartan-Hadamard manifolds to prove existence.
result Established conditions for the existence of ground states on hyperbolic space.
Model learns tool affordances from vision, enabling tool selection.
problem Learning tool affordances from visual input.
method Vision-based generative model with task predictor.
result Agents can select appropriate tools based on task success criteria.
Study of integral flows on Riemannian manifolds with focus on blow-up profiles and concentration-compactness.
problem Analyzing nonlinear integral flows on Riemannian manifolds with specific focus on blow-up profiles and concentration-compactness.
method Investigation of a family of nonlinear integral flows involving Riesz potentials, focusing on the Hardy-Littlewood-Sobolev (HLS) subcritical and critical regimes.
result Established convergence on unit spheres and certain locally conformally flat manifolds for the dual Yamabe flow.
Introduces Motion Programs for better video analysis of human motion.
problem Current video analysis focuses on raw pixels or keypoints, missing higher-level motion primitives.
method Introduces Motion Programs as a neuro-symbolic representation of motions as a composition of high-level primitives.
result Motion Programs accurately describe diverse human motions and improve downstream tasks.
In an earlier paper we showed that the space of deformations of a smooth, compact, orientable Harvey-Lawson submanifold HL in a G2 manifold M can be identified with the direct sum of the space of smooth functions and closed 2-forms on HL. In that paper, we also introduced a new class of Lagrangian-type 4-dimensional su…
Sharp HLS inequality on bounded domains with applications to curvature and isoperimetric constants.
problem Sharp Hardy-Littlewood-Sobolev inequality on bounded domains.
method Extension operator and suitable test functions.
result Existence of extremal functions and abstract domains with zero scalar curvature and larger isoperimetric constant.
Enhances MMSB for complex graph structures with HL-MRF priors.
problem Limited modeling of correlated graph structures in MMSB.
method HL-MRF as structured prior for mixed membership distributions.
result Improves log-likelihood by 15% on average across datasets.
A new generator for GANs separates style and variation.
problem Improving GANs' ability to control and disentangle image attributes.
method Borrowing from style transfer, a new generator architecture.
result Improves GANs' quality and disentanglement of latent factors.
Study uses ML to predict HL survival, outperforming CoxPH.
problem Improving survival prediction for HL patients.
method Compared multiple ML algorithms to CoxPH model.
result ML models outperform CoxPH in predicting HL survival.
MelNet generates high-fidelity audio with long-range structure.
problem Capturing long-range dependencies in audio waveforms.
method Generative model in frequency domain, leveraging spectrograms.
result Improves audio generation in various tasks.
Graph neural networks improve charged particle tracking on FPGAs.
problem Charged particle trajectory determination in high interaction density conditions.
method Graph neural networks (GNNs) embedded in tracker data as graphs, classifying edges as track segments.
result GNNs implemented on FPGAs for charged particle tracking, enabling future HL-LHC experiments.
Machine learning optimizes polymer fiber synthesis.
problem Complex material synthesis requires impractical experimentation.
method Bayesian optimisation using machine learning.
result Efficiently directs synthesis to achieve material and process objectives.
Tree-based synthesis improves forecast accuracy in GDP and inflation.
problem Improving forecast accuracy in GDP and inflation.
method Developed a nonparametric synthesis function using regression trees.
result Tree-based synthesis leads to improved forecast accuracy.
Speech synthesis from EEG features using RNN.
problem Speech synthesis from EEG data.
method Recurrent Neural Network (RNN) regression model to predict acoustic features from EEG features.
result Feasibility of synthesizing speech directly from EEG features demonstrated.