technocracy

import torch import torch.nn as nn import torch.optim as optim import numpy as np import matplotlib.pyplot as plt # ========================================== # 1. DEEP MODEL ARCHITECTURE (With Hidden Layers) # ========================================== class FortnitePolicyNet(nn.Module): def __init__(self): super(FortnitePolicyNet, self).__init__() # Added a hidden layer with 64 neurons to allow non-linear strategy mapping self.network = nn.Sequential( nn.Linear(10, 24), nn.ReLU(), nn.Linear(24, 12) ) # Safe initialization to prevent probability collapse for layer in self.network: if isinstance(layer, nn.Linear): torch.nn.init.xavier_uniform_(layer.weight) torch.nn.init.zeros_(layer.bias) def forward(self, s): logits […]

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Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods are predominantly designed for static graphs and rely on parameter averaging or distribution alignment, which implicitly assume that all features are equally transferable across clients, overlooking both the spatial and temporal heterogeneity and the presence of client-specific knowledge in real-world graphs. In this work, we identify that such assumptions create a vicious cycle […]

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arXiv:2602.02544v1 Announce Type: new Abstract: While Diffusion Language Models (DLMs) offer a flexible, arbitrary-order alternative to the autoregressive paradigm, their non-causal nature precludes standard KV caching, forcing costly hidden state recomputation at every decoding step. Existing DLM caching approaches reduce this cost by selective hidden state updates; however, they are still limited by (i) costly token-wise update identification heuristics and (ii) rigid, uniform budget allocation that fails to account for heterogeneous hidden state dynamics. To address these challenges, […]

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arXiv:2602.03948v1 Announce Type: new Abstract: In sensitive applications involving relational datasets, protecting information about individual links from adversarial queries is of paramount importance. In many such settings, the available data are summarized solely through the degrees of the nodes in the network. We adopt the $beta$ model, which is the prototypical statistical model adopted for this form of aggregated relational information, and study the problem of minimax-optimal parameter estimation under both local and central differential privacy constraints. We […]

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For years, our data visualization toolbox in Python has been dominated by two giants: Matplotlib and Seaborn. They are the undisputed champions of static, publication-quality graphics. They allow us to create beautiful, precise “photographs” of our data. But in the modern world of web-based dashboards and dynamic reports, a photograph is often not enough. Static charts are a one-way conversation. What if your charts could talk back? What if your users could hover over a data point to […]

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The digital marketing is like a forest fire, with generative AI becoming viral in other industry segments as well. It is a financial revolution. The potential of the generative AI is effective and correct and can save a large amount of time not only to accelerate the end of the month closing but also detail the financial stories. But all their novelty and convenience cannot be buried under the carpet of any cobbles of ethical problems. The Temptation […]

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Hey folks, I’m trying to get into Imitation Learning, but honestly most of what I find are dense papers 😅 I’m looking for tutorial-style resources….blogs, courses, lecture videos, or walkthroughs, that explain IL concepts clearly instead of jumping straight into theory. If you know any good resources that helped you understand things like Behavior Cloning, DAgger, or GAIL, please share! Papers are fine, but I’d really appreciate something more beginner-to-intermediate friendly. Thanks 🙏 submitted by /u/GraciaMad [link] [comments]

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Large Language Models (LLMs) are pivotal in natural language processing. The impracticality of full fine-tuning has prompted Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA), optimizing low-rank matrices A and B. In distributed scenarios where privacy constraints necessitate Federated Learning (FL), however, the integration of LoRA is often unstable. Specifically, we identify that aggregating updates from multiple clients introduces statistical variance that scales with the client count, causing gradient collapse when using high-rank adapters. Existing scaling factor candidates, […]

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