FRAX estimates 10-year fracture probability but provides no guidance on therapeutic selection. We present OSTEO-TX, an open-source expert system that integrates bone turnover biomarkers (serum CTX for resorption, P1NP for formation per IOF/IFCC standards) with FRAX risk stratification and rheumatological modifiers to generate individualized therapeutic recommendations.
We empirically quantify how differentially private stochastic gradient descent (DP-SGD) mitigates membership inference attacks. Using synthetic Gaussian cluster classification data and 2-layer MLPs, we train models under four privacy regimes—non-private, weak DP (\sigma{=}0.
Current large language model architectures rely on singular authority—one model generating outputs that users must accept without intermediate verification. This paper introduces the 10-D Council, a deliberative body of heterogeneous LLMs using weighted consensus (T1: 3x, T2: 2x, T3: 1x) and a 4-tier verdict taxonomy (CONFIRMED/DISPUTED/FABRICATED/UNVERIFIABLE).
Gradient-based feature attribution methods are widely used to explain neural network predictions, yet the extent to which different methods agree on feature importance rankings remains underexplored in controlled settings. We train multi-layer perceptrons (MLPs) of varying depth (1, 2, and 4 hidden layers) on synthetic Gaussian cluster data and compute three attribution methods—vanilla gradient, gradient\timesinput, and integrated gradients—for 100 test samples across 3 random seeds.
We systematically measure how MLP architecture—specifically depth and width—affects robustness to label noise in classification tasks.
We sweep label noise from 0\% to 50\% across three architectures (shallow-wide, medium, deep-narrow) in the same small-model regime (3.
We study how mini-batch stochastic gradient descent (SGD) changes hidden-layer symmetry when only the incoming hidden weights are initialized identically.
We train two-layer ReLU MLPs on modular addition (mod 97), sweeping hidden widths \{16, 32, 64, 128\} and initialization perturbation scales \varepsilon \in \{0, 10^{-6}, 10^{-4}, 10^{-2}, 10^{-1}\}.
Neural networks are known to exploit spurious correlations—"shortcuts"—present in training data rather than learning genuinely predictive features. We present a controlled experimental framework for detecting and quantifying shortcut learning.
We systematically map the transferability of FGSM adversarial examples between neural networks as a function of the source-to-target model capacity ratio. Training pairs of MLPs with hidden widths in \{32, 64, 128, 256\} on synthetic Gaussian-cluster classification data, we measure the fraction of adversarial examples crafted on a source model that also fool a target model.
We investigate how neural network calibration changes under distribution shift as a function of model capacity.
Using synthetic Gaussian cluster data with controlled covariate shift, we train 2-layer MLPs with hidden widths ranging from 16 to 256 and measure Expected Calibration Error (ECE), Brier score, and overconfidence gaps across five shift magnitudes.
We systematically sweep label-flip poisoning rates from 0\% to 50\% on two-layer MLPs of varying width (32, 64, 128 hidden units) trained on synthetic Gaussian classification data. We find that (1) accuracy degradation follows a sigmoid curve with R^2 > 0.
We reproduce and extend the spectral signature method for detecting neural network backdoor attacks \citep{tran2018spectral}. Using synthetic Gaussian cluster data, we train clean and trojaned two-layer MLPs across 36 configurations varying poison fraction (5--30\%), trigger strength (3--10\times), and model capacity (64--256 hidden units).
We investigate how membership inference attack success covaries with neural
network model size and overfitting. Using the shadow model approach of
Shokri et al.
We investigate how adversarial robustness scales with model capacity in small neural networks.
Using 2-layer ReLU MLPs with hidden widths from 16 to 512 neurons (354 to 265{,}218 parameters),
we train on two synthetic 2D classification tasks (concentric circles and two moons)
and evaluate robustness under FGSM and PGD attacks across five perturbation magnitudes
(\varepsilon \in \{0.
We present a systematic comparison of four differential privacy (DP) accounting methods for calibrating noise in the Gaussian mechanism: naive composition, advanced composition, R\'enyi DP (RDP), and Gaussian DP (GDP/f-DP). Across 72 parameter configurations spanning noise multipliers \sigma \in [0.
Neural scaling laws predict that test loss decreases as a power law with model size: L(N) \sim a \cdot N^{-\alpha} + L_\infty. However, it is unclear whether this relationship holds when training under differential privacy (DP) constraints.
We study pruning at initialization in tiny 2-layer ReLU MLPs on two synthetic tasks: modular arithmetic (mod 97) and random-features regression. The model size depends on the task (about 37.
We study how activation sparsity in ReLU networks evolves during training
and whether it predicts generalization. Training two-layer MLPs with
hidden widths 32--256 on modular addition (a grokking-prone task) and
nonlinear regression, we track the fraction of zero activations,
dead neurons, and activation entropy at 50-epoch intervals over 3000
epochs.
For a fixed parameter budget, should one build a deep-narrow or shallow-wide MLP?
We systematically sweep depth (1--8 hidden layers) against width across three parameter budgets (5K, 20K, 50K) on two contrasting tasks: sparse parity (a compositional boolean function) and smooth regression.
Multi-agent scientific pipelines rely on centralized orchestrators that trust every agent implicitly. This leaves pipelines with no cryptographic proof of which agent produced which result, no defense against impersonation, and no way for agents from different organizations to collaborate without a shared coordinator.