multilayer perceptron
PulseAugur coverage of multilayer perceptron — every cluster mentioning multilayer perceptron across labs, papers, and developer communities, ranked by signal.
- instance of Gotit.pub 70%
- used by Gotit.pub 70%
- used by magnetic resonance imaging 70%
- used by IArxiv 70%
- instance of PatchTST 70%
- competes with Diffusion Transformer 70%
- instance of CatalyzeX Code Finder for Papers 70%
- used by Tikhonov regularization 60%
- used by Influence Flower 60%
- used by IArxiv Recommender 60%
- used by Diffusion Transformer 50%
- competes with PatchTST 50%
22 day(s) with sentiment data
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New Invertible Logits Transformation method improves AI model calibration
Researchers have introduced Invertible Logits Transformation (InvLT), a novel post-hoc calibration method for machine learning models. InvLT applies a learned scalar MLP element-wise to pre-softmax logits, making its pa…
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Physics PhD candidate seeks transition to ML Engineering role
A Ph.D. candidate in electrical engineering with a focus on quantum optics and photonics is seeking advice on transitioning into a Machine Learning Engineering role. The individual highlights extensive software developm…
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New MRI technique enhances T2* mapping accuracy with uncertainty propagation
Researchers have developed CUPA-T2*, a novel framework designed to improve the accuracy of T2* mapping in accelerated magnetic resonance imaging (MRI). This method explicitly propagates voxel-wise uncertainty from Monte…
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Action post-training degrades VLM depth perception, research finds
A new research paper explores how action post-training affects the depth perception capabilities of vision-language models (VLMs). The study found that this post-training process, used to build vision-language-action (V…
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New foundation model FemWear targets women's health from wearable data
Researchers have developed FemWear, a specialized foundation model for women's health tasks using wearable sensor data. This model efficiently repurposes a pre-trained multimodal wearable backbone, training a small frac…
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New method detects misinformation by analyzing LLM internal representations
Researchers have developed a novel method for detecting misinformation by analyzing the internal representations of language models, rather than relying on external knowledge or surface-level text features. This approac…
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AI model for brain atrophy detection shows cross-population transferability
Researchers have investigated the transferability of a Stochastic Cortical Self-Reconstruction (SCSR) model, originally trained on UK Biobank data, to an independent Chinese population dataset. The study evaluated SCSR'…
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PyTorch profiling: Optimizing nn.Linear to fused MLPs
This article delves into the advanced profiling techniques within PyTorch, focusing on optimizing the performance of neural network layers. It specifically examines the transition from standard nn.Linear layers to fused…
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New frameworks enhance time-series forecasting with retrieval and novel architectures · 4 sources tracked
Researchers have introduced three novel frameworks for time-series forecasting, each leveraging different techniques to improve accuracy and efficiency. TimePre integrates the speed of Multilayer Perceptrons with the di…
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YOLO-PVC framework improves 3D liver tumor localization in MRI scans
Researchers have developed YOLO-PVC, a novel framework designed to improve the accuracy of 3D liver tumor localization in MRI scans. This method consolidates fragmented 2D detections from individual MRI slices into a co…
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New 'Season' framework boosts adversarial attack transferability across AI models
Researchers have developed a new framework called Season to improve the effectiveness of adversarial attacks on image recognition models. This framework specifically addresses the challenge of transferability, where att…
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New architectures enhance time series forecasting with adaptive cycle learning and hierarchical mixing
Two new research papers introduce novel architectures for time series forecasting. CAMP utilizes an adaptive cycle learning module to identify and remove dominant frequencies within each input window, followed by a hori…
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ShielDroid framework achieves 97.5% accuracy in Android malware detection
Researchers have developed ShielDroid, a novel framework for detecting Android malware through hybrid dynamic analysis. This approach analyzes application behavior in real-time to identify malicious applications that ev…
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New theory explains divergence between activation patching and weight-space ablation
Researchers have developed a theoretical framework to understand the relationship between activation patching and weight-space ablation, two methods used to determine causal responsibility in neural networks. The theory…
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AI framework extracts rich embeddings from microscopy images
Researchers have developed an AI framework to extract semantically rich image embeddings from optical microscopy images of particles and fibers. This system uses a multimodal teacher that combines visual embeddings with…
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Kolmogorov-Arnold Networks outperform MLPs in FTN signaling detection
A new research paper compares the effectiveness of Multilayer Perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs) for detecting Faster-than-Nyquist (FTN) signaling. The study generated a large dataset of nearly fou…
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KANs prove slower, costlier on embedded RISC-V than MLPs
A new research paper evaluates the deployment costs of Kolmogorov--Arnold Networks (KANs) compared to traditional Multilayer Perceptrons (MLPs) within hard-constrained recurrent physics-informed neural networks (HRPINNs…
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Simple baselines outperform deep learning in predicting CRISPRi gene perturbation effects
Researchers have identified that simple baselines can outperform complex deep learning models in predicting CRISPRi perturbation effects on held-out genes. This phenomenon was studied using the Virtual Cell Challenge be…
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New TTCD framework enhances long-context language modeling during inference
Researchers have introduced Test-Time Context Distillation (TTCD), a novel framework for long-context language modeling that optimizes parameter updates during inference. Unlike previous methods, TTCD incorporates a sel…
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RAG enhances LLMs by retrieving relevant documents, reducing hallucinations and costs
Retrieval-augmented generation (RAG) offers a method to enhance Large Language Models (LLMs) by retrieving relevant documents and including them in the prompt. This technique helps reduce hallucinations, allows for know…