Self-supervised learning (SSL) is a powerful tool in machine learning, but understanding the learned representations and their underlying mechanisms remains a challenge. This paper presents an in-depth empirical analysis of SSL-trained representations, encompassing diverse models, architectures, and hyperparameters. Our study reveals an intriguing aspect of the SSL training process: it inherently facilitates the clustering of samples with respect to semantic labels, which is surprisingly driven by the SSL objective's regularization term. This clustering process not only enhances downstream classification but also compresses the data information. Furthermore, we establish that SSL-trained representations align more closely with semantic classes rather than random classes. Remarkably, we show that learned representations align with semantic classes across various hierarchical levels, and this alignment increases during training and when moving deeper into the network. Our findings provide valuable insights into SSL's representation learning mechanisms and their impact on performance across different sets of classes.

A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either constructed from modern web data or are relatively small datasets created in the past decade by human annotators.

Why is Israel still in southern Lebanon? A war to shape Lebanon's future How Hezbollah's fibre optic drones test Israel's sophisticated radar system In the skies over the Lebanese town of Taybeh, Israel's multibillion-dollar defence systems were rendered useless by a spool of cable, according to a report by the Israeli daily Yedioth Ahronoth (Ynet). As an Israeli medical evacuation helicopter rushed to rescue soldiers wounded in a drone attack, another unmanned aerial vehicle (UAV) hurtled towards them. With their electronic countermeasures failing, soldiers on the ground were forced to point their rifles at the sky, firing at the incoming threat before it detonated just metres away. The chaotic scene underscores a lethal new reality in the escalating conflict.

A new lawsuit underscores key questions about the Tumbler Ridge killer's use of ChatGPT. A community vigil in Tumbler Ridge two days after the rural community experienced one of Canada's deadliest shootings Paige Taylor White/AFP/Getty Get your news from a source that's not owned and controlled by oligarchs. Victims of the Tumbler Ridge mass shooting and their families sued OpenAI and its CEO, Sam Altman, in US district court in San Francisco on Wednesday, claiming various negligence, product liability, and other violations. The civil complaints are the latest in a wave of litigation against OpenAI alleging that its globally popular chatbot, ChatGPT, helped people commit lethal violence. The complaints were filed by families of multiple victims wounded and killed at Tumbler Ridge Secondary School in British Columbia, Canada, where a suicidal 18-year-old opened fire on February 10.

It is commonplace to produce application-specific models by fine-tuning large pre-trained models using a small bespoke dataset. The widespread availability of foundation model checkpoints on the web poses considerable risks, including the vulnerability to backdoor attacks. In this paper, we unveil a new vulnerability: the privacy backdoor attack. This black-box privacy attack aims to amplify the privacy leakage that arises when fine-tuning a model: when a victim fine-tunes a backdoored model, their training data will be leaked at a significantly higher rate than if they had fine-tuned a typical model. We conduct extensive experiments on various datasets and models, including both vision-language models (CLIP) and large language models, demonstrating the broad applicability and effectiveness of such an attack. Additionally, we carry out multiple ablation studies with different fine-tuning methods and inference strategies to thoroughly analyze this new threat. Our findings highlight a critical privacy concern within the machine learning community and call for a re-evaluation of safety protocols in the use of open-source pre-trained models.

We study theoretical properties of a broad class of regularized algorithms with vector-valued output. These spectral algorithms include kernel ridge regression, kernel principal component regression and various implementations of gradient descent.

Fi Mini and Tractive: Which Smart Cat Tracker Should You Buy? For months, I tested Tractive and Fi Mini smart collars on my cat to find the best for activity, sleep, and GPS tracking. Wearable health-monitoring devices, like smart rings, smartwatches, and fitness trackers, help people stay on top of key wellness markers. By providing data on steps, heart rate, sleep, and more, these gadgets allow people to better understand their health, along with the opportunity to improve it with lifestyle shifts. But why should humans have all the fun?