Deep Learning
A "QuitGPT" campaign is urging people to cancel their ChatGPT subscriptions
A "QuitGPT" campaign is urging people to cancel their ChatGPT subscriptions Backlash against ICE is fueling a broader movement against AI companies' ties to President Trump. In September, Alfred Stephen, a freelance software developer in Singapore, purchased a ChatGPT Plus subscription, which costs $20 a month and offers more access to advanced models, to speed up his work. But he grew frustrated with the chatbot's coding abilities and its gushing, meandering replies. Then he came across a post on Reddit about a campaign called QuitGPT . The campaign urged ChatGPT users to cancel their subscriptions, flagging a substantial contribution by OpenAI president Greg Brockman to President Donald Trump's super PAC MAGA Inc. It also pointed out that the US Immigration and Customs Enforcement, or ICE, uses a rรฉsumรฉ screening tool powered by ChatGPT-4.
Penguin: P arallel-Packed Homomorphic Encryption for Fast Graph Convolutional Network Inference
HE operations (e.g., ciphertext (ct) rotations/multiplications, additions), which could be orders of For example, a GCN layer's computation is dominated by the special consecutive HE operations are defined in Sec. 2. For generality, we assume both feature matrix and adjacency Parallel-Packing (see Sec. 3.2), the ciphertext size is fully exploited, and the total HE operation count We adopt a threat model setting consistent with prior works [9, 14, 3, 7, 18, 22, 27]. The cloud server is semi-honest (e.g.
On Softmax Direct Preference Optimization for Recommendation Yuxin Chen 1 Junfei T an
Recommender systems aim to predict personalized rankings based on user preference data.
Have it your way: Individualized Privacy Assignment for DP-SGD
Machine learning (ML) models are known to leak information about their training data.
Modern Hopfield Networks and Attention for Immune Repertoire Classification
Michael Widrich, Bernhard Schรคfl, Milena Pavlovic, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Gรผnter Klambauer
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada
Explanation-based Data Augmentation for Image Classification
Existing works have generated explanations for deep neural network decisions toprovide insights into model behavior. Weobservethat these explanations can also be used to identify concepts that caused misclassifications. This allows us to understand the possible limitations of the dataset used to train the model, particularly the under-represented regions in the dataset.
MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space
Moreover, CNNs and Transformers struggle to capture global degradation due to their limited receptive fields.
Catch-A-Waveform: LearningtoGenerateAudio fromaSingleShortExample
Oncetrained,ourmodelcangeneraterandom samples of arbitrary duration that maintain semantic similarity to the training waveform, yet exhibit new compositions of its audio primitives.