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Found 17 papers, 5 papers with code
Multi-modal Learning for WebAssembly Reverse Engineering
WasmRev is pre-trained using self-supervised learning on a large-scale multi-modal corpus encompassing source code, code documentation and the compiled WebAssembly, without requiring labeled data.
GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU
Yet, efficient tensor-centric frameworks for GNNs remain scarce due to unique challenges and limitations encountered when implementing segment reduction in GNN contexts.
Learning to Maximize Mutual Information for Chain-of-Thought Distillation
Knowledge distillation, the technique of transferring knowledge from large, complex models to smaller ones, marks a pivotal step towards efficient AI deployment.
Sibyl: Forecasting Time-Evolving Query Workloads
Drawing insights from real-workloads, we propose template-based featurization techniques and develop a stacked-LSTM with an encoder-decoder architecture for accurate forecasting of query workloads.
Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles
Learning-based approaches to autonomous vehicle planners have the potential to scale to many complicated real-world driving scenarios by leveraging huge amounts of driver demonstrations.
Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing
Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles.
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching
One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence.
Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model
In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user's sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally.
Learning Bounded Context-Free-Grammar via LSTM and the Transformer:Difference and Explanations
With the forced decomposition, we show that the performance upper bounds of LSTM and Transformer in learning CFL are close: both of them can simulate a stack and perform stack operation along with state transitions.
N-Bref : A High-fidelity Decompiler Exploiting Programming Structures
In software development, decompilation aims to reverse engineer binary executables.
ProFlip: Targeted Trojan Attack With Progressive Bit Flips
In this work, we present ProFlip, the first targeted Trojan attack framework that can divert the prediction of the DNN to the target class by progressively identifying and flipping a small set of bits in model parameters.
Deep Symbolic Superoptimization Without Human Knowledge
Deep symbolic superoptimization refers to the task of applying deep learning methods to simplify symbolic expressions.
Coda: An End-to-End Neural Program Decompiler
Furthermore, Coda outperforms the sequence-to-sequence model with attention by a margin of 70% program accuracy.
Continuous Convolutional Neural Network forNonuniform Time Series
Convolutional neural network (CNN) for time series data implicitly assumes that the data are uniformly sampled, whereas many event-based and multi-modal data are nonuniform or have heterogeneous sampling rates.
A Neural-based Program Decompiler
Reverse engineering of binary executables is a critical problem in the computer security domain.
Towards Safety-Aware Computing System Design in Autonomous Vehicles
To address this issue, we propose a `safety score' as a primary metric for measuring the level of safety in AV computing system design.
Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA
Thus there does exist redundancy that can be exploited to further reduce the amount of on-chip computations.
