Idanwo-Aago Ikẹkọ Idanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. What happens in the cited classic test-time training adaptation procedure before the main prediction?
   • The unknown main-task label is supplied as a correct training target
   • The model must delete all parameters
   • Every earlier training example is reloaded without adaptation
   • Model parameters are updated using the incoming unlabeled sample
2. Kini idi ti TTT Ayebaye ṣe gbẹkẹle iṣẹ-ṣiṣe iranlọwọ ti ara ẹni?
   • Nitoripe abojuto ara ẹni nigbagbogbo jẹ deede diẹ sii ju iṣẹ-ṣiṣe akọkọ lọ
   • Nitoripe awọn aami ko si ni akoko idanwo, nitorinaa o nilo pipadanu o le ṣe iṣiro laisi wọn
   • Nitoripe o yọ iwulo fun eyikeyi nẹtiwọọki nkankikan
   • Lati encrypt data idanwo naa
3. What forms the recurrent hidden state of a TTT sequence layer?
   • The parameters of an inner model updated through learning
   • Only an unchanging raw input filename
   • A mandatory list of all previous full attention matrices
   • The vocabulary sorted alphabetically
4. Under fixed inner-model dimensions and update work, how does TTT sequence-layer complexity scale with sequence length?
   • Quadratically for every possible implementation
   • Independently of processing any tokens
   • Linearly in sequence length
   • Exponentially because every token retrains from all possible sequences
5. What problem motivated the cited classic test-time adaptation study?
   • A requirement to increase the alphabet size
   • A difference between training and test input distributions
   • A guarantee that all deployment inputs match training
   • A need to sort source filenames faster
6. Why is a decrease in an auxiliary adaptation loss insufficient by itself to establish a successful main-task update?
   • Every auxiliary loss is the unknown main-label accuracy
   • A loss value proves perfect safety on every future sample
   • The auxiliary objective and the intended prediction measure are different
   • An auxiliary task cannot be computed on unlabeled input
7. What inner models distinguish the studied TTT-Linear and TTT-MLP layer variants?
   • A decision tree and a mandatory image tokenizer
   • Two copies of the same fixed dictionary with no parameters
   • A full historical attention matrix and an alphabetical index
   • A linear model and a two-layer multilayer perceptron
8. In the sequence-layer framework, which loop learns the parameters defining the inner self-supervised task?
   • The outer training loop
   • Only the unknown future test label
   • A required manual dictionary edit at every token
   • Neither loop can learn any task parameters