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Found 32 papers, 13 papers with code
RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
When varying workflows, the tested LLMs show significantly different NFR-aware code generation capabilities between two workflows: (1) integrating NFRs and functional requirements into the initial prompt and (2) enhancing Function-Only-generated code with the same NFR.
Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment
Several studies in PH have applied machine learning to low-cost diagnostic tools like 12-lead ECG (12L-ECG), but they mainly focus on areas with limited resources, overlooking areas with no diagnostic tools, such as rural primary healthcare in LMICs.
An Efficient Outlier Detection Algorithm for Data Streaming
The nature of modern data is increasingly real-time, making outlier detection crucial in any data-related field, such as finance for fraud detection and healthcare for monitoring patient vitals.
Slicing Vision Transformer for Flexible Inference
Thus, we propose a general framework, named Scala, to enable a single network to represent multiple smaller ViTs with flexible inference capability, which aligns with the inherent design of ViT to vary from widths.
Radio-Based Passive Target Tracking by a Mobile Receiver with Unknown Transmitter Position
We employ a belief propagation approach to compute the posterior distributions of the positions of the scatterers and the transmitter.
Toward Adaptive Reasoning in Large Language Models with Thought Rollback
For instance, the solving rate of GPT-4 with TR outperforms the current best by $9\%$ on the \texttt{MATH} dataset.
Bilingual Adaptation of Monolingual Foundation Models
By continually pre-training on a mix of Arabic and English corpora, the model retains its proficiency in English while acquiring capabilities in Arabic.
Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
In this work, we propose a set of physics-informed geometric operators (GOs) to enrich the geometric data provided for training surrogate/discriminative models, dimension reduction, and generative models, typically employed for performance prediction, dimension reduction, and creating data-driven parameterisations, respectively.
Pearls from Pebbles: Improved Confidence Functions for Auto-labeling
We develop a tractable version of the framework to obtain \texttt{Colander} (Confidence functions for Efficient and Reliable Auto-labeling), a new post-hoc method specifically designed to maximize performance in TBAL systems.
Virtual Foundry Graphnet for Metal Sintering Deformation Prediction
Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer.
3D object quality prediction for Metal Jet Printer with Multimodal thermal encoder
With the advancements in 3D printing technologies, it is extremely important that the quality of 3D printed objects, and dimensional accuracies should meet the customer's specifications.
SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents
We use GPT3. 5 as our underlying LLM and several baselines (RawGPT, CodeT, Reflexion) to evaluate code generation on four benchmarks: HumanEval, HumanEval-ET, MBPP, and MBPP-ET.
Ranked #11 on
Code Generation
on MBPP
Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation
Predictive multiplicity refers to the phenomenon in which classification tasks may admit multiple competing models that achieve almost-equally-optimal performance, yet generate conflicting outputs for individual samples.
Studying and Recommending Information Highlighting in Stack Overflow Answers
Conclusion: Our findings suggest that it is possible to develop recommendation models for highlighting information for answers with different formatting styles on Stack Overflow.
TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance
In this paper, we propose a novel cross-modal distillation method, called TinyCLIP, for large-scale language-image pre-trained models.
BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model
BTLM-3B-8K is available under an Apache 2. 0 license on Hugging Face: https://huggingface. co/cerebras/btlm-3b-8k-base.
Substructure Aware Graph Neural Networks
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.
Ranked #11 on
Graph Regression
on ZINC
Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster
We study recent research advances that improve large language models through efficient pre-training and scaling, and open datasets and tools.
Feature selection algorithm based on incremental mutual information and cockroach swarm optimization
In this study, we propose an incremental mutual information based improved swarm intelligent optimization method (IMIICSO), which uses rough set theory to calculate the importance of feature selection based on mutual information.
Towards Better Semantic Understanding of Mobile Interfaces
Improving the accessibility and automation capabilities of mobile devices can have a significant positive impact on the daily lives of countless users.
Zero-Ending Prices, Cognitive Convenience, and Price Rigidity
Using a large store level retail CPI data, we find that 0 ending prices are popular and rigid at convenience stores even when they offer little transaction convenience.
VALHALLA: Visual Hallucination for Machine Translation
In particular, given a source sentence an autoregressive hallucination transformer is used to predict a discrete visual representation from the input text, and the combined text and hallucinated representations are utilized to obtain the target translation.
Temporal Relevance Analysis for Video Action Models
In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature.
MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate
The fluctuation effect of gradient expectation and variance caused by parameter update between consecutive iterations is neglected or confusing by current mainstream gradient optimization algorithms. Using this fluctuation effect, combined with the stratified sampling strategy, this paper designs a novel \underline{M}emory \underline{S}tochastic s\underline{T}ratified Gradient Descend(\underline{MST}GD) algorithm with an exponential convergence rate.
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation
Recent studies show that current source code authorship attribution methods can be compromised by attackers exploiting adversarial examples and coding style manipulation.
Can An Image Classifier Suffice For Action Recognition?
We explore a new perspective on video understanding by casting the video recognition problem as an image recognition task.
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection
OOF is often only detected upon careful review, potentially causing rescanning and workflow delays.
Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage.
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables.
A K-fold Method for Baseline Estimation in Policy Gradient Algorithms
The high variance issue in unbiased policy-gradient methods such as VPG and REINFORCE is typically mitigated by adding a baseline.
Neuron's Eye View: Inferring Features of Complex Stimuli from Neural Responses
Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses.
Improved Sigma Filter for Speckle Filtering of SAR Imagery
This improved sigma filter remains simple in concept and is computationally efficient but without the deficiencies of the original Lee sigma filter.
